Distance Functions and Their Use in Adaptive Mathematical Morphology

Distance Functions and Their Use in Adaptive Mathematical Morphology
Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Science and Technology 1137
Distance Functions and Their
Use in Adaptive Mathematical
Morphology
VLADIMIR ĆURIĆ
ACTA
UNIVERSITATIS
UPSALIENSIS
UPPSALA
2014
ISSN 1651-6214
ISBN 978-91-554-8923-6
urn:nbn:se:uu:diva-221568
Dissertation presented at Uppsala University to be publicly examined in 2347,
Lägerhyddsvägen 2, Hus 2, Uppsala, Friday, 23 May 2014 at 13:15 for the degree of Doctor
of Philosophy. The examination will be conducted in English. Faculty examiner: Hugues
Talbot (University Paris-Est - ESIEE).
Abstract
Ćurić, V. 2014. Distance Functions and Their Use in Adaptive Mathematical Morphology.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and
Technology 1137. 88 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-8923-6.
One of the main problems in image analysis is a comparison of different shapes in images. It is
often desirable to determine the extent to which one shape differs from another. This is usually
a difficult task because shapes vary in size, length, contrast, texture, orientation, etc. Shapes can
be described using sets of points, crisp of fuzzy. Hence, distance functions between sets have
been used for comparing different shapes.
Mathematical morphology is a non-linear theory related to the shape or morphology of
features in the image, and morphological operators are defined by the interaction between an
image and a small set called a structuring element. Although morphological operators have been
extensively used to differentiate shapes by their size, it is not an easy task to differentiate shapes
with respect to other features such as contrast or orientation. One approach for differentiation
on these type of features is to use data-dependent structuring elements.
In this thesis, we investigate the usefulness of various distance functions for: (i) shape
registration and recognition; and (ii) construction of adaptive structuring elements and functions.
We examine existing distance functions between sets, and propose a new one, called the
Complement weighted sum of minimal distances, where the contribution of each point to the
distance function is determined by the position of the point within the set. The usefulness of the
new distance function is shown for different image registration and shape recognition problems.
Furthermore, we extend the new distance function to fuzzy sets and show its applicability to
classification of fuzzy objects.
We propose two different types of adaptive structuring elements from the salience map of
the edge strength: (i) the shape of a structuring element is predefined, and its size is determined
from the salience map; (ii) the shape and size of a structuring element are dependent on the
salience map. Using this salience map, we also define adaptive structuring functions. We also
present the applicability of adaptive mathematical morphology to image regularization. The
connection between adaptive mathematical morphology and Lasry-Lions regularization of nonsmooth functions provides an elegant tool for image regularization.
Keywords: Image analysis, Distance functions, Mathematical morphology, Adaptive
mathematical morphology, Image regularization
Vladimir Ćurić, Department of Information Technology, Division of Visual Information and
Interaction, Box 337, Uppsala University, SE-751 05 Uppsala, Sweden.
© Vladimir Ćurić 2014
ISSN 1651-6214
ISBN 978-91-554-8923-6
urn:nbn:se:uu:diva-221568 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-221568)
To my mother Stana and my father Žarko
Mami i tati
List of papers
This thesis is based on the following papers, which are referred to in
the text by their Roman numerals.
I
II
Lindblad, J., Ćurić, V., Sladoje, N. (2009) On set distances and
their application to image registration, In Proceedings of the 6th
International Symposium on Image and Signal Processing and
Analysis (ISPA 2009), IEEE, pp. 449–454.
Ćurić, V., Lindblad, J., Sladoje, N., Sarve, H., Borgefors, G.
(2014) A new set distance and its application to shape registration,
Pattern Analysis and Applications, Volume 17, Issue 1, pp.
141–152.
III Ćurić, V., Lindblad, J., Sladoje, N. (2011) Distance Measures
between Digital Fuzzy Objects and Their Applicability in Image
Processing, In Proceedings of the 14th International Workshop on
Combinatorial Image Analysis (IWCIA 2011), LNCS–6636, pp.
385–397.
IV
V
VI
VII
Ćurić, V., Luengo Hendriks, C.L., Borgefors, G. (2012) Salience
Adaptive Structuring Elements, IEEE Journal of Selected Topics in
Signal Processing, Special Issue on Filtering and Segmentation in
Mathematical Morphology, Volume 6, Issue 7, pp. 809–819.
Ćurić, V., Luengo Hendriks, C.L., (2012) Adaptive Structuring
Elements Based on Salience Information, In Proceedings of the
International Conference on Computer Vision and Graphics
(ICCVG 2012), LNCS–7594, pp. 321–328.
Ćurić, V., Luengo Hendriks, C.L., (2013) Salience-Based Parabolic
Structuring Functions, In Proceedings of the 11th International
Symposium on Mathematical Morphology (ISMM 2013),
LNCS–7883, pp. 181–192.
Ćurić, V., Angulo, J., Morphological Image Regularization Using
Adaptive Structuring Functions, Manuscript for journal
publication.
VIII
Ćurić, V., Landström, A., Thurley, M., Luengo Hendriks, C.L.
Adaptive Mathematical Morphology – a Survey of the Field,
Accepted to Pattern Recognition Letters
Reprints were made with permission from the publishers.
The author has contributed considerably to method development, implementations and writing of Papers II-VIII. The author also contributed
to Paper I, but to a lesser extent. The work presented in Papers I-III were
developed under close discussions with Joakim Lindblad and Nataša
Sladoje, who also contributed in writing. The code for multi-modal image registration in Paper II was mostly developed by Hamid Sarve, and
Gunilla Borgefors contributed in writing. The work in Papers IV-VI was
developed in close discussions with Cris L. Luengo Hendriks. The author developed and implemented the methods, and wrote the papers,
but with comments and advices from the coauthors. The method in
Paper VII was developed in close discussions with Jesús Angulo. The
author implemented the method and wrote the paper. Paper VIII was
written in a close collaboration with Anders Landström. The implementations and writing was split between the two. The other coauthors
contributed in discussions and comments.
Related work
In addition to the papers included in this thesis, the author has also
written or contributed to the following publications.
1. Lindblad, J., Sladoje, N., Ćurić, V., Sarve, H., Johansson, C.B.,
Borgefors, G. (2009) Improved quantification of bone remodelling
by utilizing fuzzy based segmentation, In Proceedings of the 16th
Scandinavian Conference on Image Analysis (SCIA 2009), LNCS–
5575, pp. 750–759.
2. Ćurić, V., Heiliö, M., Krejić, N., Nedeljkov, M., (2010) Mathematical Model for Efficient Water Flow Management, Nonlinear Analysis
– Real World Applications, Volume 11, Issue 3, pp. 1600–1612.
3. Ćurić, V., Lindblad, J., Sladoje, N. (2010) The Sum of minimal
distances as a useful distance measure for image registration, In
Proceedings of the Swedish Symposium on Image Analysis (SSBA
2010), pp. 55–58.
4. Allalou, A., Ćurić, V., Pardo Martin, C., Yanik, M.F., Wählby, C.
(2011) Approaches for increasing throughput and information content of image-based zebrafish screens, In Proceedings of the Swedish
Symposium on Image Analysis (SSBA 2011), pp. 5–8.
5. Ćurić, V., Luengo Hendriks, C.L., Borgefors G. (2012) Adaptive
structuring elements based on salience distance transform, In Proceedings of the Swedish Symposium on Image Analysis (SSBA
2012), pp. 127–130.
6. González-Castro, V., Debayle, J., Ćurić, V. (2014) Pixel Classification using General Adaptive Neighbourhood-based Features, To appear in Proceedings of the 22th International Conference on Pattern Recognition (ICPR 2014), IEEE.
Contents
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.1
Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2
Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2
Distance Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1
Brief Introduction to Distance Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2
Distance Functions Between Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3
Distance Functions Between Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4
Distance Transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1
Distance Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2
Gray-Weighted Distance Transform . . . . . . . . . . . . . . . . . . . . . . .
2.4.3
Salience Distance Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5
Applications to Image Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
13
15
19
22
22
24
25
27
3
Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1
Basic Morphological Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2
Adjunction Property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3
Opening and Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4
Other morphological operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5
On the Selection of Structuring Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
34
36
37
39
40
4
Adaptive Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Adaptivity in Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2
Adjunction Property in Adaptive Mathematical
Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3
Methods for Input-Adaptive Structuring Elements and
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4
Salience-based Adaptive Mathematical Morphology . . . . . . . . .
4.5
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
43
5
Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1
Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3
Concluding Remarks and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
63
64
66
6
Brief Summaries of the Included Papers
...........................................
67
..................................................................................................
71
Appendix
45
49
50
56
Acknowledgements
................................................................................
Summary in Swedish
References
73
.............................................................................
77
......................................................................................................
79
1. Introduction
“Essentially, all models are wrong, but some are useful.”
George Edward Pelham Box
1.1 Motivation
Image analysis deals with extracting relevant information from the data
represented as digital images. This field started in the 1960’s with the
extensive use of computers that evolved in previous few decades. Today, different image analysis methods are used to solve some difficult
problems that arise in medicine, biology, astronomy, etc. Those methods benefit from using appropriate mathematical models, and therefore
underlying mathematical theories and tools are highly important.
Various mathematical models can be used in image analysis. For instance, images can be considered as matrices, and hence, tools developed in linear algebra can be used. Digital images can be represented
in a discrete grid, and methods derived in the field of discrete geometry can be a useful tool as well. Similarly, as a discrete structure, an
image can be considered as a graph, which is sometimes important for
real applications due to a lower computational cost for a method. Furthermore, an image can be considered in a continuous framework by
evolving a partial differential equation over time or using Fourier analysis.
A comparison of shapes is a problem that often appears in image
analysis. This is a difficult task because shapes vary in size, orientation,
contrast and other features. Shapes in images can be represented as
sets, and distance functions between sets give a measure for how similar
the shapes are. These distance functions can be used for various image
analysis related tasks, such as shape and image registration, and image
retrieval. Hence, it is of interest to study existing distance functions
between sets of points, and further develop new ones.
Possibly the first well-defined non-linear theory in image analysis is
mathematical morphology, which can be used for image filtering, segmentation and measurement and characterization of objects in the image. Mathematical morphology began as a technique for studying random sets with applications in the mining industry. Mathematical morphology was first defined for binary images, and then extended to graylevel and color images. Furthermore, it is defined for lattices and graphs
11
as well as in the continuous domain. Mathematical morphology operators (morphological operators) require a structuring element. Typically
this is defined as a small rigid set. The construction of adaptive structuring elements that depend on the content in the image is now a popular
and challenging task in mathematical morphology.
In this thesis, we study distance functions and their use for the construction of adaptive structuring elements. The main contributions of
this thesis are:
• We develop new distance functions between set of points and evaluate their applicability to the task of shape and image registration.
• We propose new methods for constructing adaptive structuring elements and adaptive structuring functions (adaptive morphological operators, in general) that are based on different distance functions.
• We present a unified framework for the proper computation of
adaptive morphological operators as well as the applicability of
adaptive mathematical morphology to image regularization.
1.2 Thesis outline
This thesis consists of six chapters, including the introductory chapter.
The next chapter, Distance functions, presents an overview of distance
functions between sets of points and their applicability to image registration. The third chapter, Mathematical Morphology, offers the reader
a brief overview on the field of mathematical morphology related to
this thesis. Methods for adaptive structuring elements as well as theoretical advances and applications of adaptive morphological operators
are presented in Adaptive Mathematical Morphology. Conclusions and
Future Work, discusses the contributions of the thesis and presents possible extensions and improvements of this work. Finally, the last chapter
presents short summaries of the papers included in the thesis.
12
2. Distance Functions
Distance functions have been used for many problems in image analysis and are some of the older tools. They are also used for defining
different distance transforms, which is an efficient way to calculate distances between pixels in the image. In this chapter, we consider distance
functions between sets of points, which can be computed in linear time
with respect to the number of pixels in the image. Their extension to
distance functions between fuzzy sets are also presented. We explore
the applicability of distance functions between sets of points to shape
registration and object matching.
2.1 Brief Introduction to Distance Functions
Let X be a non-empty set. A function d : X × X → R is called a metric
on X, if for all x, y, z ∈ X the following properties hold [38]:
(i) Nonnegativity
d(x, y) ≥ 0,
(ii) Symmetry
d(x, y) = d(y, x),
(iii) Reflexivity
d(x, x) = 0,
(iv) Triangle inequality
d(x, y) ≤ d(x, z) + d(z, y).
The pair (X, d) is usually called a metric space.
Some functions do not satisfy all properties of a metric, i.e., properties (i)–(iv). For instance, if a function d satisfies nonegativity (i),
symmetry (ii) and triangle inequality (iv) it is called a pseudometric,
while when satisfies properties (i)–(iii) it is called a semimetric. A function d is a distance function if it satisfies the nonegativity property (i).
Various distance functions are defined in the literature, and used for
different applications [38]. Statements in the rest of this section that
are not followed by a reference implicitly refer to Deza and Deza [38].
First, we review some well-known distance functions that will be used
later in this thesis. Let x = (x1 , ..., xn ) ∈ Rn and y = (y1 , ..., yn ) ∈ Rn ,
n ∈ N. The Euclidean metric, and possibly the most used distance
function, is defined as
! 21
n
X
2
.
(2.1)
d(x, y) =
(xi − yi )
i=1
13
(a)
(b)
(c)
Figure 2.1. Open balls (white objects) of the same radius for different distance
functions: (a) City block; (b) Euclidean; (c) Chessboard.
The Euclidean metric is a special case of Lp , p > 0 metrics defined by
! p1
n
X
.
(2.2)
dp (x, y) =
(xi − yi )p
i=1
For p = 1, this metric is called L1 distance or City block distance. When
p → +∞, then dp converges to the Chebyshev distance or L∞ distance,
defined by
d∞ (x, y) = max{|xi − yi | : i = 1, ..., n}.
(2.3)
This distance is also known as the Chessboard distance, when one considers x, y ∈ Zn .
Given a metric space (X, d), the open ball with the centre x0 and
radius r > 0 is defined by
B(x0 , r) = {x : d(x0 , x) < r}.
(2.4)
The open balls for different Lp metrics are shown in Figure 2.1.
Let P(a, b) = {a = x1 , ..., xn = b} be a path between points a and b in
X, where xi and xi+1 , i = 1, ..., n − 1, are adjacent points in the path.
The cost of the path P(a, b) is defined by
c(P(a, b)) =
n−1
X
c(xi , xi+1 ),
(2.5)
i=1
where the distance function c is the cost to travel between two adjacent
points in the path, i.e., the value c(xi , xi+1 ) corresponds to the cost to
travel from a point xi to a point xi+1 . The length of a shortest path from
a point a to a point b is the minimal cost c of all paths between these
two points, i.e., the length is defined by
min{c(P(a, b)) : P(a, b) ∈ Π},
(2.6)
where Π denotes the set of all paths between points a and b. This type
of a distance function is usually refer to as path-based distance.
14
2.2 Distance Functions Between Sets
A number of different distance functions are defined between sets of
points [38]. Here, we consider distance functions that are computable
in linear time with respect to the number of points in the set, and we
investigate their applicability to shape registration.
A distance between two sets is often based on the point-to-set distance. Let A and B be two non-empty, closed and bounded subsets of
X. The distance between a point a ∈ X and a set B ⊂ X is defined as
^
d(a, B) =
d(a, b),
(2.7)
b∈B
V
W
where
denotes the infimum. In the rest of the thesis, the symbol
denotes the supremum. Note that, the point-to-point distance d(a, b)
can be any metric. An element b0 ∈ B is called an element of best
approximation to a given point a if
^
d(a, b0 ) =
d(a, b),
(2.8)
b∈B
i.e., if the infimum is attained.
The earliest distance defined between two sets is possibly the Hausdorff metric, here denoted with dH , [49]
!
_
_
d(a, B),
d(b, A) .
(2.9)
dH (A, B) = max
a∈A
b∈B
It is often considered that the Hausdorff metric is introduced in 1914,
when this metric was presented in Hausdorff’s famous book [49]. However, the first appearance of a similar distance function, that is nowadays called the Hausforff metric, can be found in the PhD thesis of
Pompeiu in 1905 [76]. Therefore, some authors suggest that this metric should be called the Hausdorff–Pompeiu metric or the Pompeiu–
Hausdorff metric [9, 11].
The Hausdorff metric is based on an element with the largest distance
to the other set (see Figure 2.2 for 2D binary objects). This implies that
the Hausdorff metric is highly sensitive to outliers, i.e., this metric depends on a few points that are far from the bulk. We show this property
of the Hausdorff metric in Paper I and Paper II. Nonetheless, this metric
is very widely used. An extensive list of disciplines where the Hausdorff
metric has been used was recently listed by Berinde and Pacurar [9].
This is a really impressive list of disciplines ranging from mathematics
to biophysics and economics.
Different modifications of the Hausdorff metric have been proposed
in the literature to deal with its aforementioned issues with outliers. An
15
Figure 2.2. Illustration of the Hausdorff distance for two sets represented as
2D shapes. The Hausdorff distance is based on two maximal point-to-set distances for sets A and B, i.e., the
W Hausdorff distance is the
W maximum of the two
d(b, A).
following values: d(a, b0 ) =
d(a, B) and d(b, a0 ) =
a∈A
b∈B
empirical evaluation of different modifications of the Hausdorff metric
was presented by Dubuisson and Jain [41]. One modification of the
Hausdorff metric, that is the least sensitive to noise in images, is called
the Modified Hausdorff distance, dMH , and defined as
dMH (A, B) = max
!
1 X
1 X
d(a, B),
d(b, A) ,
|A|
|B|
a∈A
(2.10)
b∈B
where |A| and |B| denote the cardinality of the sets A and B, respectively. dMH is not a metric, since the triangle inequality is not satisfied [41]. This distance function is less sensitive to noise due to its
definition as the mean of point-to-set distances for all points from both
sets, rather than taking the supremum of those point-to-set distances,
which is used in the definition of the Hausdorff metric (2.9).
A distance similar to the Modified Hausdorff distance is the Sum of
minimal distances, dSMD , defined by Eiter and Mannila [42],
1
dSMD (A, B) =
2
X
a∈A
d(a, B) +
X
b∈B
!
d(b, A) .
(2.11)
This distance is not a metric, since it does not satisfy the triangle inequality [42]. As we show in Paper I, this distance function is less sensitive to outliers than the Hausdorff metric. This is especially important
for image analysis tasks such as shape matching and image registration,
which will be discussed in Section 2.5.
As can be seen from the definition of the Hausdorff metric (2.9), this
distance function is dependent on the boundaries of objects (see Figure 2.2). The Chamfer matching distance, dCH , was defined by Borge16
fors [16] as
dCH (A, B) =
X
d(a, ∂B),
(2.12)
a∈∂A
where ∂A and ∂B are the boundaries of sets A and B, respectively. This
distance function is not a metric, since the symmetry property is not
satisfied. Furthermore, this distance function is computationally less
expensive than previously introduced distance functions, since it uses
only points from the boundaries of the sets. On the other hand, it can
be very sensitive to noise that influences the object boundary, as we
show in Paper I and Paper II.
Apart from the distance functions defined for sets by the point-to-set
distance, they can be defined in some other ways too. Probably the
easiest way to define a distance between two sets is to take the infimum
of all the distances between any two of their respective points, i.e.,
^
dINF(A, B) = {d(x, y) : x ∈ A, y ∈ B}.
(2.13)
This distance function is not suitable for comparing sets. For instance,
the distance between two sets with non-empty intersection is zero, being independent of the cardinality of the intersection between these
sets.
The symmetric difference between sets A and B, defined by A△B =
(A \ B) ∪ (B \ A) provides information about these two sets. The metric that corresponds to this set operation, here denoted with dSD, was
defined by Klette and Rosenfeld [58] as
dSD (A, B) = |A△B|.
(2.14)
Its main disadvantage is that it does not take into account information
about the spatial position of the points in the set, i.e., the position of the
shape in the image, which is a specific application that we are looking
for in this thesis. For instance, the distance between two disjoint binary
objects is the same, independently how far the objects are, as it depends
only on cardinality of the objects. Hence, the usefulness of this distance
function for shape comparison is limited.
The above distance functions between sets (except the one defined by
(2.13)) are compared in an empirical study on rigid body registration
of binary shapes in Paper I. In that paper, we introduce the notion of the
complement distance function d, defined as d(A, B) = d(A, B), where
A and B are complements of sets A and B, respectively, with respect to
the universe X. The complement distance d adds new information to a
distance d if and only if d 6= d. For instance, dSD = dSD and dSMD 6= dSMD .
We explore different combinations of dSMD and dSMD , which are the two
distance functions with best properties for image registration (among
17
(a)
(b)
(c)
(d)
Figure 2.3. (a) Binary set A; (b) Binary set B; (c) Contribution of every point
from sets A and B to dSMD (A, B); (d) Contribution of every point from sets A
and B to dCW (A, B).
the ones compared in Paper I). Our experiments show that it is better
to incorporate complements of the sets on a point level, rather than at
the whole set level.
Following this conclusion, in Paper II, we propose the Complement
weighted sum of minimal distances as a weighted version of the Sum
of minimal distances, where each point in the set contributes to the
distance function according to its spatial position within the set. The
underlying idea is to define a distance function in which points that are
deeper inside of the object have higher importance and contribution to
the distance (see Figure 2.3). This assumption is suitable for problems
in image analysis, since points close to the boundary of an object are
usually more affected by noise. Therefore, to each point a of a set A
is assigned a weight dependent on its position within the set A, i.e., a
weight is calculated as d(a, A). The weighted contribution of all points
of set A is normalized by the sum of all given weights. The contribution
of all points from set B is computed in a similar way. The Complement weighted sum of minimal distances, dCW , between sets A and B is
defined as (Paper II)
P
d(a, B)d(a, A)
1  a∈A
+
dCW (A, B) = 
P
2
d(a, A)
a∈A
18

d(b, A)d(b, B)

b∈B
P
.
d(b, B)
P
b∈B
(2.15)
Figure 2.4. Crisp (left) and fuzzy (right) representation of the same object.
Crisp object is obtained from the fuzzy one taking the α−cut for α = 0.5.
This distance is a semimetric because the triangle inequality is not satisfied. The denominators are non-zero, since for a non-empty set C ⊂ X,
it holds that d(c, C) 6= 0, c ∈ C.
2.3 Distance Functions Between Fuzzy Sets
Broadly speaking, a fuzzy set is a generalization of a crisp set. An element either belongs to a crisp set, or not, while the belongingness of
an element to a fuzzy set can be partial, and it is described by a value
between zero and one. The theory of fuzzy sets is a good mathematical model for gray-level images in general, as well as for more specific
image analysis problems as in fuzzy segmentation, which produces objects with non-sharp boundaries [75]. For instance, a crisp and a fuzzy
representation of the same discrete object are shown in Figure 2.4.
Let us briefly introduce fuzzy set theory. A fuzzy set A on a reference
set X, is a set of ordered pairs A = {(x, µA (x)) : x ∈ X}, where µA :
X → [0, 1] is the membership function of the fuzzy set A, [120]. An
α−cut of a fuzzy set A, is the set αA = {x ∈ X : µA (x) ≥ α}, α ∈ (0, 1].
The height of a fuzzy set A is h(A) = max µA (x), while the support of
x∈X
A is defined as Supp(A) = {x ∈ X : µA (x) > 0}. The complement A of
a fuzzy set A, is A = {(x, 1 − µA (x)) : x ∈ X}.
There exist several ways to define a distance function between two
fuzzy sets, and a good overview on this topic was presented by Bloch [12].
Two different groups of distance functions between fuzzy sets were considered in that paper. The first group is composed of methods that
are based solely on comparing membership functions of two fuzzy sets
(A, µA ) and (B, µB ), and the best known are Lp , p > 0 distances, defined by
1
X
p p
.
(2.16)
|µA (x) − µB (x)|
d(A, B) =
x∈X
Several other distances of this type are listed in a more recent paper [119].
19
Distance functions that combine membership functions and spatial
information of the support of fuzzy sets belong to the second group.
Taking the spatial information into account, these distance functions
are more suitable for image analysis applications, and such distance
functions are explored in Paper III. In that paper, we present extensions
of the distance functions between crisp sets of points that have the best
performance for image registration (Paper I and Paper II), which are the
Sum of minimal distances, dSMD , and the Complement weighted sum of
minimal distances, dCW .
The most natural way to extend distance functions between crisp sets
to distance functions between fuzzy sets is to consider integration over
α−cuts [79]. Integration over α−cuts is a general principle for extending properties and relations on crisp sets to corresponding ones on fuzzy
sets. Using the integration over α−cuts, a distance function between
two fuzzy sets A and B is defined as
α
d (A, B) =
Z
1
d(αA,αB) dα,
(2.17)
0
where d is a distance function between crisp sets of points.
Following the above discussion and using (2.17), the Sum of minimal distances, dSMD , and the Complement weighted sum of minimal
distances, dCW , are defined for fuzzy sets as (Paper III)
dα
SMD (A, B) =
dα
CW (A, B)
=
Z
Z
1
dSMD (αA,αB) dα,
(2.18)
dCW (αA,αB) dα.
(2.19)
0
1
0
The main drawback with this approach is that dα (A, B) = ∞ if the
heights of the two fuzzy sets are not the same, i.e., h(A) 6= h(B). This
problem also occurs when d is the Hausdorff metric in (2.17). Several
modifications have been proposed to solve this issue [20, 30], but none
of them is widely accepted [21].
Despite that most distance functions between sets rely on the pointto-set distance (Section 2.2), the distances between two fuzzy sets are
rarely defined using the point-to-set distance for fuzzy sets. Two definitions of the point-to-set distance for fuzzy sets were proposed by
Bloch and Maı̂tre [13]. The first definition is based on integration over
α−cuts, where the distance between a point a and a fuzzy set B is defined as
Z 1
Z 1 ^
d(a, b) dα.
(2.20)
d(a, B) =
d(a,αB) dα =
0
20
0 b∈αB
The second definition is based on weighting the points from the support,
Supp(B), of the fuzzy set B with their membership values,
^
d(a, B) =
d(a, b) · F (µB (b),
(2.21)
b∈Supp(B)
where F (t) is a decreasing function of t. Note that the point-to-point
distance d(a, b) is the spatial distance between two points and is independent on their membership values. A point-to-set distance can also
be defined using morphology for fuzzy sets, where the value of such a
distance is a fuzzy number [13].
When (2.20) is used, the Sum of minimal distances and the Complement weighted sum of minimal distances for fuzzy sets are, respectively,
defined as (Paper III)


X
X
1

(2.22)
d(a, B) +
d(b, A) ,
dps
SMD (A, B) =
2
a∈Supp(A)
dps
CW (A, B) =

P
b∈Supp(B)
d(a, B) · d(a, A)
1  a∈Supp(A)
P

2
d(a, A)
a∈Supp(A)
+
P
d(b, A) · d(b, B)
b∈Supp(B)
P
b∈Supp(B)
d(b, B)


.
(2.23)
In Paper III, we use only the definition (2.20) of the point-to-set distance. We tried different functions including F (t) = 1 − t, F (t) = 1 − t2
and F (t) = e−t for the point-to-set distance defined by (2.21), but we
did not observe any good performance. Furthermore, if a ∈ Supp(B),
then for b = a
d(a, b) = 0 ⇒ d(a, b) · F (µ(b)) = 0 ⇒ d(a, B) = 0,
(2.24)
which reduces the usefulness of this distance definition for the task of
object matching. Therefore, we do not use the point-to-set distance
defined by (2.21).
When computing the point-to-set distance (2.20), we assumed that
µ(a) = 1 for all points a, and in this way the membership value of the
observed point in the point-to-set distance is neglected. This membership value was recently considered when defining a point-to-set distance
for fuzzy sets [64], by defining
Z µ(a)
d(a, B) =
d(a,αB) dα.
(2.25)
0
Another definition of a point-to-set distance is based on the length of
the shortest path from a fuzzy point to a fuzzy set. This approach,
21
being based on the shortest paths, becomes more natural since it is consistent with the distance computations in images (Section 2.4). The
Sum of minimal distances and the Complement weighted sum of minimal distance were defined using the last two definitions for point-toset distances for fuzzy sets, and their usefulness for the task of template matching and object classification was presented by Lindblad and
Sladoje [64].
2.4 Distance Transforms
In general, an image can be represented by a function f : D ⊂ Rn →
Rm , n, m ∈ N. A binary image can be seen as a function f defined as
follows
1,
x ∈ A,
f (x) =
(2.26)
0,
x∈
/ A,
where A ⊂ D. A gray-level image can be considered as a function f
when m = 1, while for color or multivalued images m > 1.
The distance transform is an image operator that computes distances
between points in the image. A distance transform is usually considered
to be defined for binary images, while for gray-level images one can
define a gray-weighted distance transform or a geodesic distance transform. Furthermore, a distance transform can be computed for color
images, too. In these distance transforms, the underlying point-to-point
distance determines the distance transform, as will be seen later in this
section.
In this section, we first review the distance transform for binary and
gray-level images. Then, we present the salience distance transform
that is used in Chapter 4 and for the methods that are proposed in Paper
IV, Paper V and Paper VI. Note that we present distance transforms for
2D images, but they can be computed for arbitrary dimensions [14].
2.4.1 Distance Transform
To keep presentation simple, we will present distance transforms for
two-dimensional digital images. Let (X, d), X ⊂ Z2 be a metric space
and A ⊂ X. And let f be a binary image defined by (2.26). Then, the
distance transform of an image f is defined as
^
d(x, y), x ∈ X.
(2.27)
DT [f ](x) =
y∈A
Note that equation (2.27) corresponds to the point-to-set distance d(x, A)
defined by (2.7). A distance transform can be seen as an image, where
22
(a) Mask M1
(b) Mask M2
Figure 2.5. Masks for the computation of a distance transform for 3 × 3 neighbourhood.
each pixel in the object has a positive numerical value that corresponds
to its distance to the background.
The basic idea, utilized for most distance transforms, is to approximate the global distance by propagation of local steps, i.e., distances between neighbouring points in the image. This approach was presented
by Rosenfeld and Pfaltz [86]. A distance transform is dependent on the
predefined weights (cost) to move from a pixel to the neighbouring one,
and it can be computed by propagating local steps using the two-pass
chamfering algorithm, where the weights are given by two masks [86].
An example of masks and weights for 3 × 3 neighbourhood is shown
in Figure 2.5. Here, we present the case for 2D images, but a similar
approach can be used for images of higher dimensions [15]. Prior to
running the chamfering algorithm, the elements in the object are set to
zero and the elements in the background are set to infinity. Next, the
image is scanned from the upper left corner, first right then down (forward scan) using the mask M1 (see Figure 2.5 (a)), followed by a scan
from the lower right corner (backward scan) using the mask M2 (see
Figure 2.5 (b)).
The distance for a pixel a is computed by propagating distance values
from the neighbouring pixels in the distance map DT. This process is
performed by adding the weight for the local step. The distance in a
point a is the minimum value of itself and the values in the neighbourhood increased by respective local weights, i.e.,
DT(a) = min (DT(a + b) + w(b)),
b∈M1
(2.28)
where b is a vector from a to pixels in the mask M1 , and w(b) is the
corresponding weight given in the mask. The final result is computed
using the second (backward) scan by
DT(a) = min (DT(a + b) + w(b)).
b∈M2
(2.29)
The size of the mask and the weights determine the distance transform. For instance, for a mask of size 3×3, the pair w = hw1 , w2 i denotes
23
the weights in the mask, where w1 is the weight for edge neighbours
and w2 is the weight for vertex neighbours (see Figure 2.5). Although
it seems natural to use the Euclidean distance for the weights, these are
not the best weights to use since this distance transform overestimates
long distances. Instead, Borgefors [15] recommended to use weights
h0.95509, 1.36930i or the integer weights h3, 4i, which have better properties. The use of the weights h1, 2i results in the City block distance,
while weights h1, 1i yield to the Chessboard distance. It is possible to
utilize the same concept by having larger neighbourhoods [106], as well
as for other grids different from Zn [107]. Note that the Euclidean distance transform can be computed in linear time with the respect to the
number of pixels in the image [70].
2.4.2 Gray-Weighted Distance Transform
The computational process of a distance transform for gray-level images f : X ⊂ Z2 → R is a bit different than for binary images. A 2D
gray-level image can be represented as a surface, denoted here with S,
embedded in 3D space, with two spatial coordinates and one coordinate
that represents the gray-level value in the image. A geodesic distance
between two points (a, f (a)), (b, f (b)) ∈ S, a, b ∈ Z2 is the shortest path
between two points along the surface [57]. A distance transform that
computes the geodesic distance between points in the image is called a
transform on gray-level surfaces [56].
Various different geodesic distances have been considered in the literature [56, 63, 91, 100, 109]. Similarly to the distance transform for
binary images, the geodesic distances are based on the local step (cost)
to move from one point to the neighbouring one along the surface S.
The cost can be dependent on two incommensurate domains: (i) the
spatial positions of pixels; (ii) gray-level values for pixels. For instance,
in Section 4.4. we use the following cost c for the construction of adaptive structuring elements [109]
c(xi , xi+1 ) = d(xi , xi+1 ) + σ|f (xi ) − f (xi+1 )|,
(2.30)
where d is a spatial distance between two neighbouring points xi and
xi+1 in a path that connects points a and b. The parameter σ > 0 makes
the two domains commensurate. Then, the geodesic distance between
the points a and b is computed with (2.6) by taking the minimal cost of
all possible paths between these points.
The main difference between the distance transform and the grayweighted distance transform is how they can be computed. While the
distance transform can be computed by the two-pass chamfering algorithm, the gray-weighted distance transform requires performing a
24
number of iterations until the stability of the distance is obtained. One
way to compute the shortest path between all points in an image is by
using Dijkstra’s algorithm [39]. Another method for distance computations is the fast marching algorithm [97]. This method is based on
propagating a wave front from a set of pixels by using partial differential equations, in particular the Eikonal equation. In addition, the
computational cost to compute the gray-weighted distance transform is
O(N log N ), where N is the number of pixels in the image.
2.4.3 Salience Distance Transform
The classical distance transform uses as an input a binary image that
might be obtained by thresholding. On the other hand, the salience
distance transform eliminates the need for binarization, and it incorporates edge attributes such as strength (gradient magnitude), length or
curvature. To keep the presentation simple, we focus on the approach
where the salience distance transform is computed using the strength
of the edges and without other attributes of the edges.
The underlying assumption of the salience distance transform is that
the edges are weighted by their importance, where stronger edges have
higher importance, i.e., salience. Several ways of incorporating salience
into the distance transform have been studied by Rosin and West [88].
Three algorithms were proposed in that study, and each one uses the
two-pass chamfering algorithm.
The first algorithm ([88], Algorithm 1) is the simplest one. The distance transform and the propagated magnitude of the edges are computed separately and stored in two images as follows:
b̃ = arg min(DT(a + b) + w(b)),
b∈M1
DT(a) = DT(a + b̃) + w(b̃),
MP(a) = MP(a + b̃) + w(b̃),
where DT(a) and MP(a) are the classical distance transform and the
propagated magnitude in a point a, respectively. The initialization for
the salience distance transform is similar to the classical distance transform, where the elements of the background (non-edge points) are set
to infinity. The edge points are set to zero for the distance transform DT,
and to the initial edge strength for the propagated magnitude MP. After
the forward scan, the backward scan should be performed in a similar
manner. Then, the salience distance transform is computed by simply
dividing the classical distance transform with the propagated magnitude, i.e., SDT(a) = DT(a)/MP(a), for each point in the image. Since
25
(a)
(b)
(c)
Figure 2.6. Salience distance transform computed with Algorithm 2. (a) Input
image; (b) Edge strength image; (c) Salience distance transform.
the propagated edge magnitude map MP is discontinuous, the resulted
salience distance transform SDT is also discontinuous.
Similarly to the first algorithm, the authors proposed an algorithm
([88], Algorithm 3) that propagates distance as well as magnitude of
the edges in the same local step of the two-pass chamfering algorithm.
The salience distance transform is computed during the propagation
process by dividing the propagated distance with the propagated edge
magnitude, repeatedly as
b̃ = arg min
b∈M1
DT(a + b) + w(b) ,
MP(a + b)
DT(a) = DT(a + b̃) + w(b̃),
MP(a) = MP(a + b̃) + w(b̃).
This initialization and the computation process is the same as for the
above described algorithm, i.e., the resulted salience distance transform
is computed as SDT(a) = DT(a)/MP(a). This algorithm is the most
computationally expensive one, but it produces the salience distance
transform that is continuous.
Rosin and West also proposed an algorithm ([88], Algorithm 2) that
takes the edge strength image as an input (Figure 2.6 (b)), where the
edge pixels are initialized with the negative values of their magnitudes.
The non-edge pixels are set to infinity, similarly to as in the distance
transform. Then, the salience distance transform is computed with the
classical two-pass chamfering algorithm (Figure 2.6 (c)). The salience
map produced with this algorithm is continuous.
An alternative algorithm to compute the salience distance transform
is to use a threshold decomposition of the input edge image [87]. For
this algorithm, the salience distance transform is computed as the sum
of distance transforms over a threshold decomposition of the edge magnitude image; the result of this algorithm is dependent on the number of
26
(a)
(b)
Figure 2.7. The salience distance transform with respect to edge strength using
Algorithm 2, for 1D function. (a) Edges are far from each other; (b) Edges are
close to each other.
thresholds used. The resulting salience distance transform is zero at the
edge pixels, and the magnitude becomes irrelevant at those points. The
same conclusion is valid for Algorithms 1 and 3. However, the salience
distance transform obtained by Algorithm 2 has non-zero values for the
edge pixels, which makes a distinction between edges based on their
salience.
Being based on the magnitude of edges in the image, the salience distance transform is dependent on the spatial positions of the edge points.
If a weak edge is far from a strong one, i.e., one with higher magnitude,
then the resulting salience distance transform might be influenced by
the weaker edge (Figure 2.7 (a)). However, if two edges are close to
each other, the weaker one might be overshadowed by the stronger one
(Figure 2.7 (b)).
The salience distance transform is a good alternative to gray-weighted
distance transform, taking advantage of easy computations of the classical distance transform by incorporating salience in the chamfering
algorithm. The resulting salience map is continuous (for most of the
presented algorithms), which is a desirable property when constructing
adaptive structuring elements (Section 4.4). For this purpose, we use
the second algorithm presented here ([88], Algorithm 2), where the
edge strength is considered as salience.
2.5 Applications to Image Registration
The process of aligning two images of the same object is far from easy,
since images, even those acquired with the same imaging technique, often have large variations in illumination, pose, noise, etc. Moreover, if
objects are imaged with different imaging modalities, they have different characteristics. Generally speaking, image registration is the process
of finding the best geometric transformation that aligns two images, one
27
called an observed image O, and another a reference image R. The registration is often considered as a process that seeks the transformation
that maximizes a similarity measure between two images [103]. Instead, the geometric transformation that minimizes a distance function
between the images is good because it simplifies the search. Hence,
the registration process can be identified by the following minimization
problem
arg min d(O, T (R; y)),
(2.31)
y
where d is a distance function, T is a geometric transformation and y belongs to the set of all reasonable transformation parameters. The choice
of geometric transformation depends on the application. In the rigid
body case, i.e., when the object is not deformed between the different
imaging occasions, the transformation typically includes rotation and
translation. On the other hand, if that is not the case, the registration
is considered to be non-rigid, i.e., the process where the objects might
be deformed between imaging occasions. A rigid-body registration is
often required as the first step in a non-rigid registration problem. Extensive studies on different image registration methods were presented
by Zitova and Flusser [121] and by Hajnal and Hill [47].
One of the key problems in image registration is how to find an appropriate distance function, which is the underlying requirement for a
good registration algorithm, since even very sophisticated optimization
algorithms do not have good performance with an inadequate distance
function. A number of different distance functions have been used for
image registration. For instance, the most commonly used intensitybased distance functions are the sum of squared differences and cross
correlation; they are useful for registration of images that are acquired
in the same modality [8, 90]. The distance function called mutual information is used for multi-modal image registration [65, 118]. Similarly, the distance called normalized gradient fields was proposed for
multi-modal image registration problems [46]. The Gromov–Hausdorff
distance was recently used for non-rigid shape comparison [24]. Of
the distance functions presented in Section 2.2, the Hausdorff metric [54, 55] and the Chamfer matching distance [26, 93] have been
used for rigid-body registration.
In this thesis, we consider distance functions between sets that can be
computed in linear time (Section 2.2), and evaluate their usefulness for
rigid-body shape registration. Desirable properties of a distance function used for this task are:
(1) The distance between the object and a transformed version of the
object monotonically increases with increasing translation and rotation of the object.
(2) The distance function has low sensitivity to noise.
28
(a)
(b)
(c)
Figure 2.8. The Chamfer matching distance does not distinguish between
points of the object and points of the background. (a): Reference image R,
(b): Observed image O, (c): Observed image (blue color) and registered image (orange color) superimposed. The elements that belong to both shapes are
shown in gray.
The first property enforces that an optimization process (2.31) has only
one minimum, which is the global one. The second property is important for real world applications, where images are often corrupted by
noise.
The best way to evaluate the distance functions presented in Chapter
2.2, especially considering property (1), is to use a greedy search in the
registration process. Therefore, we use this simple optimization algorithm since the objective of Paper II is to compare distance functions,
and this method emphasizes properties of distances. An ideal situation
would be that the distance function has only one global minimum. This
distance behaviour is often impossible in practice, and the algorithm
might get stuck in a local minimum. Therefore, we also evaluate how
far the local minima are from the desirable global one. We perform
four different experiments to show the usefulness of the Complement
weighted sum of minimal distances to image registration (Paper II).
The first evaluation is conducted on a set of binary shapes with different displacements and noise conditions. For most of the experiments,
the Complement weighted sum of minimal distances has the best performance, followed by the Sum of Minimal distances (see Paper II, Table
1). The Hausdorff distance has a low rate of perfect registrations, especially when shapes are degraded by noise. The Chamfer matching
distance, being based just on object boundaries, is highly influenced by
the boundary noise, and the greedy search usually got stuck in a local
minimum not far from the global one (see Paper II, Figure 9). Also, the
Chamfer matching distance does not differentiate between points of the
object and points of the background, which may lead to a local minimum at a position where the registered objects have a large common
boundary while the interiors are not well matched (see Figure 2.8).
29
(a)
(b)
(c)
Figure 2.9. Multi-modal image registration. (a) Histological slice; (b) Corresponding slice in 3D SRµCT volume found by the Complement weighted sum
of minimal distances; (c) Visualization of this 3D SRµCT volume.
We note an interesting property of the studied distance functions:
the registration is occasionally better for shapes with noise than for the
noise free case, i.e., noise sometimes removes local minima. This property of distance functions shows their imperfections, and that the selection and construction of a “perfect” distance function for a particular
task is not an easy problem.
In Paper II, we also present three examples of real registration problems in which the Complement sum of minimal distances outperforms
other distance functions presented in Chapter 2.2: (i) Recognition of
handwritten digits by using the nearest neighbour classification algorithm. A shape is correctly classified if the reference shape with the
minimal distance is the same digit as the observation shapes. (ii) Registration of 2D images extracted from synchrotron radiation micro computed tomography (SRµCT) bone implants volume (Figure 2.9 (c)). (iii)
Multi-modal registration of the exact location of a 2D histological slice
of a bone implant within a 3D SRµCT volume of the same implant (Figure 2.9).
In Paper III, we introduce the Complement weighted sum of minimal distances and Sum of minimal distances for fuzzy sets. We perform
distance-based object classification (nearest neighbour rule) for crisp
and fuzzy discrete object representations of discrete disks and octagons.
Note that a fuzzy discrete representation of objects (Figure 2.10 (c) and
(f)) often appears visually more similar to the corresponding continuous objects (Figure 2.10 (a) and (d)) than a crisp discrete representation at the same resolution (Figure 2.10 (b) and (e)). Fuzzy representations of disks and octagons were generated using pixel coverage
digitization [98] of continuous crisp disks and octagons, respectively.
In the pixel coverage representation, the membership of a pixel is equal
to the relative area of the pixel that is covered by the object [98]. It
has been shown that this representation provides a good framework for
higher precision of estimation of different measures [99]. The corre30
(a)
(b)
(c)
(d)
(e)
(f)
Figure 2.10. Crisp and fuzzy discrete representations of continuous objects: (a)
Continuous disk; (b) Crisp discrete disk; (c) Fuzzy discrete disk; (d) Continuous octagon; (e) Crisp discrete octagon; (f) Fuzzy discrete octagon.
sponding crisp object representations are obtained by taking the α−cut
at α = 0.5, of the fuzzy object representations (Figure 2.10).
The recognition, i.e., correct classification rate, of the fuzzy discrete
objects using distances between fuzzy sets (Section 2.3) is better than
for the discrete objects of the same spatial resolution, using the corresponding distances between crisp sets. We also show that the performance of distance functions between fuzzy sets depends on the number
of membership levels used for fuzzy set. Obviously, more membership
levels provide more information to the distance function, but this will
not necessarily lead to the improvement of a distance function for a
particular task (Paper III).
31
3. Mathematical Morphology
This chapter presents the basics of mathematical morphology, and introduces the notation used in the following chapters. Mathematical
morphology was introduced in mid 1960s by two French scientists,
Georges Matheron and Jean Serra, who developed the main concepts
and tools [69, 95, 96]. Since then, mathematical morphology has been
widely used in image analysis, and it is an integral part of almost all
basic image analysis courses. A number of excellent books exist on this
topic including ones by Soille [102], and Najman and Talbot [74].
Mathematical morphology is based on theories and tools from various
branches of mathematics. The field began as a technique for analysing
binary images with applications in the mining industry. When mathematical morphology was introduced, it was based on set-theoretical
notions. Since then, the field developed to gray-level images using the
notion of umbra transform [105], and later using the notion of complete lattices [53, 83]. Nowadays, mathematical morphology is defined
on various structures such as multivalued images [6], graphs [50, 115]
and manifolds [80], and it can be defined using partial differential equations [23].
In this thesis, we consider mathematical morphology for gray-level
images, i.e., for functions f : D ⊂ Rn → R. We review basic morphological operators that use the set S ⊂ Rn to probe the image under
study. The set S is referred to as structuring element. Furthermore, such
structuring element is often referred to as flat structuring elements because it has two dimensions in the case of 2D images, i.e., when n = 2.
The shape and size of a structuring element are chosen depending on
the application. Initially, structuring elements were fixed, i.e., one structuring element is used for every point in the image. In the rest of the
thesis, we will refer to such structuring elements as rigid. Since the
selection of structuring elements is not always an easy task, it recently
became an increased interest to define structuring elements that adapt
to structures and orientations in the image. Chapter 4 is devoted to this
topic.
A structuring element is defined with respect to the origin. The origin allows the positioning of the structuring element at a given point.
Structuring element at point x ∈ D means that its origin coincides with
the point x. By translating a structuring element to a point x, its origin
will coincide with x, i.e., Sx = {b + x : b ∈ S}. It should be stressed
33
(a) S1
(b) S2
(c) S3
Figure 3.1. Different structuring elements in Z2 : (a) Square structuring element of side 5; (b) Discrete disk of radius 3; (c) Structuring element that does
not contain its origin. The origin of these structuring elements is marked with
X.
here that a structuring element does not necessarily contain its origin.
In Figure 3.1, we show three different structuring elements in Z2 .
Since an n dimensional gray-level image f corresponds to an n + 1
dimensional surface, the structures in the image can be investigated
with mathematical morphology using functions s : D ⊂ Rn → [−∞, 0].
These functions can be seen as structuring elements with weights assigned to each point. These functions are called non-flat structuring
elements, or structuring functions. Similarly to structuring elements, it
is important to define the origin of a structuring function.
Statements in the rest of this chapter that are not followed by a reference implicitly refer to Soille [102].
3.1 Basic Morphological Operators
Let L be a set of all gray-level images with the domain D ⊂ Rn , i.e.,
L = {f |f : D → R}. Let (L, ≤) be a complete lattice with a partial
order “≤” defined by
f ≤ g ⇔ (∀x ∈ D) f (x) ≤ g(x),
(3.1)
where f, g ∈ L. A lattice is complete if all subsets have both an infimum
and a supremum.
Each structuring element S has one corresponding structuring element S ∗ , called reflected or transposed structuring element, that is simply the reflection of S through the origin. That means, S ∗ = {−b : b ∈
S}. For instance, S1 = S1∗ , S2 = S2∗ , S3 = S3∗ (see Figure 3.1) The
two basic morphological operators are the erosion εS : L → L and the
dilation δS : L → L defined as [95]
^
[εS (f )](x) =
f (y), x ∈ D,
(3.2)
y∈Sx
34
(a) Input f
(b) Erosion εS2 (f )
(c) Erosion εS3 (f )
(d) Dilation δS1 (f )
(e) Dilation δS2 (f )
(f) Dilation δS3 (f ).
Figure 3.2. Morphological operators with different structuring elements that
are shown in Figure 3.1.
[δS (f )](x) =
_
f (y),
x ∈ D.
(3.3)
∗
y∈Sx
We now turn our focus to the non-flat case, i.e. structuring functions.
Let sx : D → [−∞, 0] be an arbitrary structuring function with the
origin in x. Some typical examples of structuring functions are linear
(cone) function sx (y) = −d(x, y) and quadratic (parabolic) function
sx (y) = −d(x, y)2 = −kx − yk2 .
The corresponding reflected structuring function is defined as [19]
sx (y) = sy (x),
(3.4)
x, y ∈ D.
The erosion and the dilation by the structuring function sx are then
defined as [95]
^
[εs (f )](x) =
(f (y) − sx (y)) , x ∈ D,
(3.5)
y∈D
[δs (f )](x) =
_
(f (y) + sy (x)) ,
x ∈ D.
(3.6)
y∈D
It should be noted that the flat case is given by expressing a structuring element S using a corresponding structuring function s defined
as
0,
y ∈ Sx ,
(3.7)
sx (y) =
−∞,
y∈
/ Sx ,
35
which, inserted into (3.5) and (3.6), gives (3.2) and (3.3), respectively.
In Figure 3.2, we present the erosion and the dilation using structuring elements depicted in Figure 3.1. The erosion shrinks light objects in
the image, and expands black objects (see Figure 3.2 (b) and (c)). The
dilation has the opposite effect (see Figure 3.2 (d), (e) and (f)). These
two morphological operators satisfy several important properties. First,
the erosion and the dilation are dual operators, i.e.,
εS (−f ) = −δS (f ),
f ∈ L.
(3.8)
Both operators preserve ordering of the functions, i.e., the property
called increasingness is satisfied
f ≤g ⇒
εS (f ) ≤ εS (g) and δS (f ) ≤ δS (g) , f, g ∈ L. (3.9)
Extensivity of the dilation and antiextensivity of the erosion are satisfied
if the structuring element S contains the origin, i.e.,
εS (f ) ≤ f ≤ δS (f ),
f ∈ L.
(3.10)
If the origin is not included in the structuring element S, the morphological operators can produce unintuitive result (see Figure 3.2 (c) and
(f)).
3.2 Adjunction Property
Two operators ε : L → L and δ : L → L form an adjunction (ε, δ) when
δ(f ) ≤ g ⇔ f ≤ ε(g),
f, g ∈ L.
(3.11)
The adjunction property (3.11) is one of the most fundamental notions
in mathematical morphology. This property can be considered as the
morphological counterpart of Galois connection [37]. Also, the adjunction property is closely related to the notion of complete lattices.
Erosion is any operator ε : L → L that preserves the infimum, i.e.,
!
^
^
ε(Xi), Xi ∈ L,
(3.12)
Xi =
ε
i∈I
i∈I
where I is an index set. Dilation is any operator δ : L → L that preserves the supremum, i.e.,
!
_
_
δ(Xi ), Xi ∈ L.
(3.13)
Xi =
δ
i∈I
36
i∈I
(a) Input f
(b) Opening γS2 (f )
(c) Closing φS2 (f )
Figure 3.3. Opening and closing with the discrete disk of radius three as structuring element (S2 from Figure 3.1 (b)).
It can be proven that any two operators ε and δ that form an adjunction (ε, δ) are an erosion and a dilation, respectively [83]. Moreover,
for every dilation δ there exists one and only one erosion ε that forms
an adjunction (ε, δ). Similarly, for every erosion ε there exists a unique
dilation δ that forms an adjunction (ε, δ) [83].
Two different approaches exist to define an erosion and a dilation. In
one approach (presented in Section 3.1), for one operator a structuring element must be replaced with its reflected structuring element (see
(3.2) and (3.3)), while for the other approach this is not required, i.e.,
the same structuring elements is used to compute an erosion and a dilation. These approaches differ in the sense that the basic morphological
operators, erosion and dilation, satisfy the adjunction property (3.11)
when they are defined by the first one, which is not generally true for
the later one.
3.3 Opening and Closing
Combinations of the two basic morphological operations, erosion and
dilation, with the same structuring element, lead to two other important
morphological operators: opening and closing. Opening can be defined
by
γS = δS ◦ εS ,
(3.14)
and closing by
φS = εS ◦ δS .
(3.15)
Opening removes bright regions in which structuring element S does
not fit, while closing removes dark regions in which S does not fit. Figure 3.3 (b) and (c)) depicts an example of opening and closing.
Similarly to erosion and dilation, opening and closing are dual operators, i.e.,
γS (−f ) = −φS (f ), f ∈ L.
(3.16)
37
Furthermore, they are idempotent
γS ◦ γS = γS ,
(3.17)
φS ◦ φS = φS ,
increasing,
f ≤ g ⇒ γS (f ) ≤ γS (g) and φS (f ) ≤ φS (g) ,
f, g ∈ L,
(3.18)
and opening is antiextensive, i.e.,
γS (f ) ≤ f,
f ∈ L,
(3.19)
f ∈ L.
(3.20)
and closing is extensive, i.e.,
f ≤ φS (f ),
If the origin belong to the structuring element S then the following
ordering relation is valid
εS (f ) ≤ γS (f ) ≤ f ≤ φS (f ) ≤ δS (f ).
(3.21)
Morphological operators defined so far are translation invariant, that
is, the two following operations give the same result: (1) first, a morphological operator is applied, and the image is then translated; (2)
first, the input image is translated, and the morphological operator
(same as in (1)) is applied. Mathematically,
ψS ◦ Tt = Tt ◦ ψS ,
(3.22)
where ψ is a morphological operator and Tt (C) = {x + t : x ∈ C}, for a
set C.
An operator that is increasing, idempotent, anti-extensive and can be
defined as an erosion followed by a dilation (3.14) is often referred to
as morphological opening. If an operator has the same properties, but
cannot be written as a unique erosion followed by dilation, then this
operators is called an algebraic opening. An algebraic opening can be
written as the supremum of a family of morphological openings as [69]
_
Γ(f ) =
γSi (f ),
(3.23)
i∈I
where {Si : i ∈ I} is a family of structuring elements. Some of algebraic openings are area opening [116], path opening [52], rank-max
opening [101]. For instance, area opening removes all connected components with an area that is smaller than a certain number of points.
38
(a) Input f
(b) Gradient GS2 (f )
Figure 3.4. Morphological gradient with the discrete disk of radius three as
structuring element S2 .
3.4 Other morphological operators
Erosion and dilation, can be combined in various ways to form powerful
morphological operators. Apart from basic morphological operators,
other morphological tools were used for a number of image analysis
tasks. Here, we briefly summarize a few that will be used later in this
thesis.
One of the useful morphological operators, that will be used in Chapter 4.5, is the morphological gradient, defined by
GS (f ) = δS (f ) − εS (f ),
f ∈ L.
(3.24)
Similarly to the gradient magnitudes, the morphological gradient is an
operator that indicates the strength of the edges in the image (see Figure 3.4).
An operator ψ is a morphological filter if and only if ψ is increasing
and idempotent. In other words, a filter preserves the order, and the
structures that are preserved by the filter will not be modified by further
use of the same filter. It is obvious that morphological closings and
openings are morphological filters.
The granulometry is an approach to compute a size distributions of
grains, and it is obtained using a family of openings {γλ : λ ≥ 0} with
increasing size of λ. This family of openings is a granulometry if the
semi-group law (absorption property) is satisfied, i.e.,
γµ ◦ γλ = γmax{µ,λ} ,
(3.25)
or, equivalently, γλ ≤ γµ if 0 ≤ µ ≤ λ. For instance, if we consider the
Euclidean space, the family of openings {γλS : λ > 0}, with convex sets
as structuring elements {λS : λ > 0} satisfies the semi-group law (3.25)
[69].
Alternating sequential filters are morphological filters that are constructed by a series of openings and closings first with a small size of
structuring elements and then with an increasing size until a certain
39
size is reached. The white alternating sequential filter that begins with
an opening is defined by
(φSn ◦ γSn ) ◦ (φSn−1 ◦ γSn−1 ) ◦ ... ◦ (φS1 ◦ γS1 ),
(3.26)
On the other hand, the black alternating sequential filter, which starts
with a closing, is defined as
(γSn ◦ φSn ) ◦ (γSn−1 ◦ φSn−1 ) ◦ ... ◦ (γS1 ◦ φS1 ).
(3.27)
Structuring elements should satisfy S1 ⊂ S2 ⊂ ... ⊂ Sn , for both alternating sequential filters.
Morphological operators can be connected with distance functions,
presented in Chapter 2. The Hausdorff distance (2.9) between two nonempty sets A and B can be written as
dH (A, B) = min{r : A ⊂ δSr (B), B ⊂ δSr (A)},
(3.28)
where Sr denotes a disk of radius r. Furthermore, it is shown by Sternberg [104] that the distance transform of a set A can be computed as
the erosion of a function f defined as
0,
x ∈ A,
f (x) =
(3.29)
∞,
x∈
/ A,
with the cone structuring function sx (y) = −d(x, y). Moreover, the
erosion of f with the parabolic structuring function sx (y) = −d(x, y)2
gives the squared distance transform [111].
3.5 On the Selection of Structuring Elements
Generally speaking, any shape and size can be considered for a structuring element, and a structuring element contains its origin or not (Figure 3.1 (c)). Morphological operators presented in this section are computed using one rigid structuring element for all points in the image.
Those morphological operators and mathematical morphology we will
refer to as classical in the rest of the thesis.
The selection of structuring element depends on the application. For
instance, circular structuring elements are well suited for images with
circular objects, as well as for any case where isotropic operation is
needed; and line segments are useful for images with a certain orientation. Also, morphological operators with larger structuring elements
preserve only large features in the image, while those with smaller
structuring elements preserve also the finer details in the image.
Despite that morphological operators are a very useful tools in image
analysis, morphological operators computed with one rigid structuring
40
element cannot differentiate shapes with respect to their contrast or
orientation. For instance, classical morphological operators cannot distinguish same-size shapes that have different contrast. Hence, it is beneficial to use structuring elements that adapt their size and shape to the
local features in the image such as contrast, luminance, the magnitude
of the gradient, orientations in the image, or to the local curvature.
Such structuring elements are called adaptive.
41
4. Adaptive Mathematical Morphology
4.1 Adaptivity in Mathematical Morphology
In its original form, morphological operators are: (i) translation invariant (3.22); (ii) based on one rigid structuring element that is translated
to every point in the image and does not depend on its position. Hence,
two possible ways to generalize mathematical morphology were discussed by Roerdink [82]:
(i) Translation invariance can be replaced by other types of invariance; this approach leads to group morphology.
(ii) Rigid structuring elements can be replaced by adaptive structuring
elements; this approach leads to adaptive mathematical morphology, also called adaptive morphology.
The basic morphological operators, defined in the classical way (Chapters 3.1 and 3.3), are translation invariant. If other types of invariance
are considered, then structuring elements change their size and shape
with respect to different geometric transformations that generate an algebraic structure called a group. Therefore, the name of this field of
mathematical morphology is called group morphology [81]. A comprehensive overview on group morphology is given by Heijmans [51].
In this thesis, we study adaptive mathematical morphology, which
is a challenging topic that attracted a lot of attention in recent years
[17, 19, 33, 60, 62, 72, 82]. Most likely, one of the earliest studies
of adaptive mathematical morphology was presented by Serra [96].
Other early work was given by Charif-Chefchaouni and Schonfeld [29],
who considered a general theory of adaptive morphology for binary
images. More recently, comprehensive theoretical results of adaptive
mathematical morphology were presented by Bouaynaya et al. [17],
and Bouaynaya and Schonfeld [19], which were further explored by
Roerdink [82]. Roerdink [82], among the other important aspects of
adaptive mathematical morphology, addressed theoretical issues important for the development of the field by noticing often overlooked issues when defining adaptive morphological operators. This paper by
Roerdink [82] can be seen as a cornerstone paper for further development of adaptive mathematical morphology, since it presents a way to
properly define morphological operators with adaptive structuring elements, i.e., under which conditions the erosion and the dilation computed with adaptive structuring elements will form an adjunction.
43
In the following text, we will refer to morphological operators computed with adaptive structuring elements as adaptive morphological operators. For instance, the erosion computed with adaptive structuring
elements will refer to as adaptive erosion.
Adaptive structuring elements can be dependent on various image attributes such as spatial position in the image, image content, orientation
in the image, etc. To distinguish different types of adaptive structuring elements, we follow the terminology introduced by Roerdink [82],
who considered two categories of adaptiveness for structuring elements:
location-adaptive structuring elements and input adaptive structuring
elements.
Location-adaptive structuring elements
These structuring elements are dependent only on the position in the
image domain D, and not on the input image f . In other words, these
adaptive structuring elements are fixed a priori and do not depend on
the image content. These adaptive structuring elements are also called
extrinsic [33]. Most likely, one of the earliest examples of adaptive
mathematical morphology was presented by Beucher et al. [10], who
defined adaptive structuring elements that are dependent on the position in the image. Vehicles are analysed, and the ones at the bottom of
the image appear larger than the ones at the top, therefore structuring
elements vary linearly with their vertical position in the image. Furthermore, Verly and Delanoy [114] and Cuisenaire [32] designed adaptive
structuring elements using the similar approach.
If structuring elements are not computed from the input image, like
the case with location-adaptive structuring elements, but rather following some law regarding the imaging device (e.g. if structuring elements
are proportional to the distance to the imaging device), the adaptive
morphological operators might not be translation invariant. In other
words, the equation (3.22) is not valid in the general case, i.e.,
ψ ◦ Tt 6= Tt ◦ ψ,
(4.1)
where ψ is a morphological operator.
Input-adaptive structuring elements
These structuring elements are dependent on the image content and
thereby the structuring elements are also dependent on the location in
the image. These adaptive structuring elements are also called intrinsic [33], and they are usually computed from a smoothed version of
44
the input image f , called a pilot image [62]. Several interesting methods for input-adaptive structuring elements have been proposed including morphological amoebas [62], region growing structuring elements
[72], general adaptive neighbourhoods [33], elliptical adaptive structuring elements [60], salience adaptive structuring elements (Paper IV),
etc.
As pointed by Roerdink [82], adaptive morphological operators are
translation invariant if adaptive structuring elements are computed from
the input (pilot) image. For instance, the results will be the same if we
first translate both the input and the pilot image, and then compute the
adaptive erosion or if we translate (using the same translation as above)
the output of the adaptive erosion.
It should be mentioned here that adaptive morphological operators,
i.e., operators that do not process all points in the image identically,
can also be defined using other tools. For instance, for morphological
operators defined by partial differential equations, adaptivity can be
incorporated directly into the partial differential equations underlying
the basic morphological operators [22, 67].
More details about different approaches in adaptive mathematical
morphology can be found in an overview paper presented by Maragos
and Vachier [68]. In Paper VIII, we present the recent developments of
adaptive mathematical morphology, including latest methods for adaptive structuring elements as well as proper definitions of adaptive morphological operators. We also present an application-oriented study of
different methods for adaptive structuring elements, and briefly discuss
possible future directions in which this field might further develop (Paper VIII).
4.2 Adjunction Property in Adaptive Mathematical
Morphology
To obtain an adjunction (ε, δ) in the classical mathematical morphology,
given by (3.11), for the erosion (3.2) and the dilation (3.3) a structuring element S used for the erosion is replaced by its reflected structuring
element S ∗ for the dilation (Chapter 3.2). On the other hand, this construction can be problematic in the context of adaptive morphology and
it is often overlooked in the literature, as noted by Roerdink [82].
Let f : D ⊂ Rn → R be an input image and f 0 : D ⊂ Rn → R be
a pilot image, which is obtained by smoothing the input image f . Let
{Sf 0 [x] : x ∈ D} be a set of adaptive structuring elements computed
from a pilot image f 0 . To each point x ∈ D is associated a unique
structuring element Sf 0 [x]. The adaptive structuring elements derived
45
for different points may be independent one from another, i.e., they may
contain neighbouring points or not.
The adaptive morphological erosion ε : L → L and the corresponding
dilation δ : L → L, are defined as [82]
^
f (y), x ∈ D,
(4.2)
[ε(f )](x) =
y∈Sf 0 [x]
[δ(f )](x) =
_
f (y),
x ∈ D,
(4.3)
y∈S ∗0 [x]
f
where Sf∗0 [x] is the reflected structuring element of Sf 0 [x] defined as
y ∈ Sf 0 [x] ⇔ x ∈ Sf∗0 [y].
(4.4)
First of all, note that if Sf 0 [x] = S for all x ∈ D, i.e., if one considers
the same rigid structuring element for all points in the image, the erosion (4.2) and the dilation (4.3) lead to the classical erosion (3.2) and
the classical dilation (3.3). Hence, it is obvious that the adaptive mathematical morphology is a generalization of the classical mathematical
morphology.
Adaptive morphological operators ε and δ, defined by (4.2) and (4.3)
will form an adjunction (ε, δ), if the structuring elements {Sf 0 [x] : x ∈
D} are computed once from the pilot image f 0 , and then used to compute the adaptive erosion ε and the adaptive dilation δ. Here, we give a
proof that ε and δ are adjunct morphological operators [82]:
δ(f ) ≤ g ⇔ [δ(f )](x) ≤ g(x), x ∈ D
_
(equation (4.3)) ⇔
f (y) ≤ g(x), x ∈ D
y∈S ∗0 [x]
f
⇔ f (y) ≤ g(x),
y ∈ Sf∗0 [x], x ∈ D
(equation (4.4)) ⇔ f (y) ≤ g(x),
^
⇔ f (y) ≤
x ∈ Sf 0 [y], y ∈ D
g(x),
y∈D
x∈Sf 0 [y]
(equation (4.2)) ⇔ f (y) ≤ [ε(g)](y),
⇔ f ≤ ε(g).
y∈D
Since the adaptive erosion ε and the adaptive dilation δ, given by
(4.2) and (4.3), respectively, form an adjunction (ε, δ), the products ε◦δ
and δ ◦ ε will lead to operators that satisfy the algebraic properties of
the closing and opening, respectively. Furthermore, it is now possible to
properly define alternative sequential filters, and other morphological
filters.
46
Figure 4.1. Adaptive structuring elements derived for points x, y and z. Since
x ∈ Sf 0 [y] then y ∈ Sf∗0 [x]; and since x ∈
/ Sf 0 [z] then z ∈
/ Sf∗0 [x].
In other words, if we first compute the erosion with adaptive structuring elements derived from the pilot image, then derive new adaptive
structuring elements from the eroded image and use them to compute
the dilation, the resulted operator will not necessarily be an opening,
i.e., not necessarily satisfy the algebraic properties of opening (3.17)–
(3.19).
Once adaptive structuring elements {Sf 0 [x] : x ∈ D} are derived
from the pilot image f 0 , it is not necessary to compute their reflected
structuring elements {Sf∗0 [x] : x ∈ D} in order to compute the adaptive
erosion and its adjoint dilation. For instance, the adaptive erosion ε
assigns to the centre x the infimum of f over the structuring element
Sf 0 [x]. This process may be called centripetal, as it brings to the centre
x all gray-level values of their neighbours in Sf 0 [x].
We observe that a point x is a part of the reflected structuring element Sf∗0 [y] for any point y within the structuring element Sf 0 [x], if
x ∈ Sf 0 [y] (see Figure 4.1). Then, the adaptive dilation can be computed as follows: The value f (x) is distributed to all points y ∈ Sf 0 [x]
and contributes to their value of the dilation. In this process, the point
y receives the contribution of all centres x of all structuring elements
Sf 0 [x] which contain y. This process may be called centrifugal, as it
distributes the value of the central pixels to their neighbours. Note that
/ Sf∗0 [x], and f (z) do not contribute to the dilation
if x ∈
/ Sf 0 [z] then z ∈
in a point x (see Figure 4.1).
The adaptive erosion ε and adaptive dilation δ can be computed as
follows [62]:
Erosion:
[ε(f )](x) = +∞, ∀x ∈ D
for each point x ∈ D do
compute Sf 0 [x]
for each point y ∈ Sf 0 [x] do
[ε(f )](x) = min{f (y), [ε(f )](x)}
47
end for
end for
Dilation:
[δ(f )](x) = −∞, ∀x ∈ D
for each point x ∈ D do
for each y ∈ Sf 0 [x] do
[δ(f )](y) = max{f (x), [δ(f )](y)}
end for
end for
Let us now consider the case when adaptive structuring functions
(non-flat structuring elements) are derived from the pilot image f 0 ,
here denoted with {sxf0 : x ∈ D}. In the theory provided by Bouaynaya
and Schofield [18, 19], non-adaptive morphological operators based on
rigid structuring functions (Chapter 3) are directly generalized to the
adaptive case. Hence, the erosion and dilation with adaptive structuring functions are defined, respectively, as
^
(4.5)
[ε(f )](x) =
(f (y) − sxf0 (y)), x ∈ D,
y∈D
[δ(f )](x) =
_
(f (y) + syf 0 (x)),
(4.6)
x ∈ D.
y∈D
Similarly to the case with adaptive structuring elements one has to
fix adaptive structuring functions once when they are computed from
the input (pilot) image. The erosion ε given by (4.5) and the dilation
δ given by (4.6) form an adjunction (ε, δ). The proof for this claim is
given by Bouaynaya and Schofield [19]:
δ(f ) ≤ g ⇔ [δ(f )](x) ≤ g(x), x ∈ D
_
(f (y) + syf 0 (x)) ≤ g(x),
(equation (4.6)) ⇔
x∈D
y∈D
⇔ f (y) + syf 0 (x) ≤ g(x),
x, y ∈ D
syf 0 (x),
sxf0 (y),
x, y ∈ D
⇔ f (y) ≤ g(x) −
y, x ∈ D
(x = y) ⇔ f (x) ≤ g(y) −
^
(equation (4.5)) ⇔ f (x) ≤
(g(y) − sxf0 (y)), x ∈ D
y∈D
⇔ f (x) ≤ [ε(g)](x),
⇔ f ≤ ε(g).
48
x∈D
4.3 Methods for Input-Adaptive Structuring
Elements and Functions
In this section, we briefly present a number of different methods for
adaptive structuring elements and adaptive structuring functions that
exist in the literature. For more details, see provided reference and Paper VIII. In general, adaptive structuring elements are defined using different features in the image, including gray-level image values, edges,
gradients, the Hessian, orientation and connectivity between points.
Hence, adaptive structuring elements often rely on a local similarity between neighbouring points or are aligned to edges and contours in the
image. Apart from being defined using different attributes in the image,
various constraints can be imposed to the shape and size of structuring
elements, ranging from being adaptivity to the image content to being
constrained by a set of predefined shapes. In this last case, the size and
orientation of these structuring elements are adapted to the image.
In Paper VIII, we distinguish the two main groups of methods for
the construction of adaptive structuring elements by being based on
different features in the image: (i) local similarity; (ii) local structure.
The methods that belong to the first group are based on a local similarity between the origin of the structuring element and its neighbouring points. A structuring element includes points that are similar to the
origin according to some measure of similarity between points. For instance, a structuring element in a point x is defined as a connected component that contains points with similar gray-level values to f (x) [33].
These adaptive structuring elements are called general adaptive neighbourhoods [35]. Structuring elements are also defined as balls in a
geodesic metric space (often called geodesic balls), where a geodesic
distance can be defined using different image attributes. For example,
morphological amoebas are computed using a geodesic distance that
takes spatial distance and gradient into account [62], while Grazzini
and Soille [45] utilized the same attributes with different geodesic distances. In the same line, we use a geodesic distance to define salience
adaptive structuring elements, which are derived from the salience map
based on the edge strength (Paper IV). Furthermore, region growing
structuring elements include points with a region growing technique
that is based on the difference between gray-level values between the
adjacent points [72].
Structure-based methods belong to the second group, where adaptive structuring elements are aligned to the structure in the image, such
as edges and contours. These methods are characterized by considering orientation in the image, distances to the neighbouring edges and
rate of anisotropy. For instance, Tankyevych et al. [108] proposed line
structuring elements where their orientations are obtained from a func49
tion that depends on the Hessian. Verdú-Monedero et al. [113] defined adaptive structuring elements (lines and rectangles) that are constrained in width by the distance to the nearby edges, where orientation of structuring elements is obtained using diffusive squared gradient
fields. Furthermore, adaptive elliptical structuring elements are derived
using the structure tensor, where their shape vary from disks to lines
depending on the rate of anisotropy [60].
Adaptive structuring elements can differ in shape and size. For instance, shapes of some adaptive structuring elements are completely
adaptive to the content of the image (still dependent on the input parameter that determines the size), such as morphological amoebas [62]
and general adaptive neighbourhoods [35], while certain constraints
are assigned for other methods [60, 113]. In addition, adaptive structuring elements can be defined to have one shape with adaptive sizes determined by the image content as proposed by Dokládal and Dokládalová
[40], which used the distance transform to determine the size of rectangles. Similarly, we use the salience distance transform to determine
the size of adaptive structuring elements, where any shape can be used
for structuring elements (Paper V).
Adaptive structuring functions are not well explored as adaptive structuring elements, and there exists only a few methods for the construction of adaptive structuring functions. Non-flat adaptive structuring elements, i.e., adaptive structuring functions are mostly inspired by the
well-known filtering methods, by considering similarity between points
or between image patches. In this line, spatially variant bilateral structuring functions [2] are related to bilateral filtering [110], while nonlocal structuring functions [92, 112] are directly related to non-local
means filtering method [25]. Recently, Angulo and Velasco-Forero [5],
recently proposed adaptive structuring functions that are based on random walks, where the step from one point to the other depends on the
distance between points, and different distance functions were considered for this task. In paper VI, we propose parabolic structuring functions based on the salience map of the edge strength.
4.4 Salience-based Adaptive Mathematical
Morphology
Our methods for constructing adaptive structuring elements (Paper IV
and Paper V) as well as for adaptive structuring functions (Paper VI) are
based on the salience map, denoted here with SM, which is a result of
the salience distance transform (Algorithm 2, Chapter 2.4) applied to
the edge strength of the input image.
50
(a) NMS(f )
(b) SDT(f )
(c) SDT+ (f )
(d) SM(f )
Figure 4.2. Computation of the salience map SM(f ) for a one dimensional
function f (see text).
To preserve most of the edges in the image, we use the gradient
estimation and non-maximal suppression from the Canny edge detector [27]. In this process, we use Gaussian derivatives to estimate the
gradient in the input image, but exclude the hysteresis thresholding
from the Canny edge detector. This approach preserves even the edges
with a small response in the gradient image. Formally, NMS(f ) is the
image obtained by computing the gradient magnitude and non-maximal
suppression of the input image f . The edge pixels are initialized with
the negative values of their salience and the non-edge pixels are set to
infinity [88]. The salience distance transform SDT(f ) is computed with
the classical two-pass chamfering algorithm [15] (Figure 4.2 (b)). After the salience distance transform is propagated from − NMS(f ), the
distance image is offset to all positive values and the map SDT+ (f ) is
obtained (Figure 4.2 (c)). By inverting the values of SDT+ (f ), we obtain the salience map SM(f ) (Figure 4.2 (d)), which can be formally
written as
_ [SM(f )](y) = Offset +
NMS(f )(x) − d(x, y) , y ∈ D, (4.7)
x∈D
where
Offset =
^ _ y∈D x∈D
NMS(f )(x) − d(x, y) ,
(4.8)
and d(x, y) is a spatial distance. For instance, the example of the salience
map SM(f ) for “Le fifre” image is shown in Figure 4.3.
51
(a) Input f
(b) NMS(f )
(c) SDT+ (f )
(d) SM(f )
Figure 4.3. Steps for computing the salience map SM for the “Le fifre” image.
The salience map SM contains the information about the spatial distance between points in the image and preserves the information about
the salience of the edges in the image, where the largest values in the
salience map SM correspond to the strongest edges in the input image.
This salience map can be computed in linear time with respect to the
number of pixels in the image N , i.e., O(N ), since the salience distance
transform can be computed in linear time.
Our main inspiration to use the salience map SM for constructing
adaptive structuring elements comes from the approach used for morphological amoebas [62]. Morphological amoebas are geodesic balls in
a geodesic (gray-weighted) distance metric space, where the distance
between two adjacent points xi and xi+1 is defined as
ca (xi , xi+1 ) = 1 + σ|f (xi ) − f (xi+1 )|,
(4.9)
where σ > 0 is a constant used to scale the two incommensurate domains. For morphological amoebas, the following definition was also
used [117]
ca (xi , xi+1 ) = d(xi , xi+1 ) + σ|f (xi ) − f (xi+1 )|,
(4.10)
where d(xi , xi+1 ) is the Euclidean distance or a weighted distance, i.e.,
h3, 4i distance [15]. Then, a morphological amoeba Ar (x) centred in a
52
(a)
(b)
Figure 4.4. Salience adaptive structuring elements for one dimensional function f . (a) Salience adaptive structuring elements for points x1 and x2 , with
the same radius r; (b) Salience adaptive structuring elements for points x1 and
x2 , with with adaptive radii, where r1 < r2 .
point x is defined as
Ar (x) = {y ∈ D : min ca (P(x, y)) < r},
P(x,y)
(4.11)
where the cost of the path P(x, y) is computed by using formulas (4.9)
or (4.10), and r is the parameter that determines the size.
Morphological amoebas adapt well to the content in the image, i.e.,
they are flexible in shape and strictly align to strong edges in the image (see Paper VIII, Figure 2). Furthermore, morphological amoebas
stretch over the points with similar gray-level values rather then points
in the neighbourhood, and structuring elements for neighbouring points
might have completely different shapes. Our intention is to construct
adaptive structuring elements such that structuring elements for two
neighbouring points have similar shapes and not completely different
ones. For this purpose, we use the salience map SM(f ) that is a continuous map, and SM(f ) gradually differs for neighbouring points.
Here, we briefly present our methods for adaptive structuring elements and adaptive structuring functions that are based on the salience
map described above. More details about the methods can be found in
Paper IV, Paper V and Paper VI of this thesis.
In Paper IV, we define adaptive structuring elements, called the salience
adaptive structuring elements, that are dependent on the path-based
distances computed on the salience map SM. We define the cost of the
path between two adjacent points xi and xi+1 as
cs (xi , xi+1 ) = [SM(f )](xi ) + [SM(f )](xi+1),
(4.12)
53
(a)
(b)
(c)
(d)
Figure 4.5. Morphological operators with salience adaptive structuring elements, (a) Input image f ; (b) Dilation for r(x) = 5 · max(SM(f )) − [SM(f )](x),
x ∈ D; (c) Erosion for r(x) = 5 · max(SM(f )) − [SM(f )](x), x ∈ D; (d) Erosion
for r(x) = 9 · max(SM(f )) − [SM(f )](x), x ∈ D.
and the salience adaptive structuring element centered in a point x is
defined as
Sr (x) = {y ∈ D : min cs (P(x, y)) < r}.
(4.13)
P(x,y)
Our main idea is that structuring elements that are close to salient edges
should be smaller in size. Therefore, structuring elements located close
to edges in the input image are smaller in size, while structuring elements in more homogeneous areas of the input image are larger (see
Figure 4.4 (a)).
We also define that the radii of the salience adaptive structuring elements is adaptive and also dependent on the salience map SM. This is
opposite to the other methods for adaptive structuring elements that use
a fixed radius for each point in the image [45, 62] (or tolerance [33]).
For example, we used r(x) = k · max(SM(f )) − [SM(f )](x), x ∈ D, to
define adaptive radii, where k > 1 is the input parameter. Two salience
adaptive structuring elements with adaptive radii are depicted in Figure 4.4 (b).
54
(a)
(b)
(c)
Figure 4.6. Morphological operators with salience-based rhomboidal (diamond) shapes as adaptive structuring elements. (a) Input image; (b) Erosion
for preserving weak edges; (c) Dilation for preserving weak edges.
We present a comparison between morphological amoebas and salience adaptive structuring elements in Paper IV. An application-oriented
study on different methods for adaptive structuring elements, including
morphological amoebas [62], general adaptive neighbourhoods [35],
adaptive elliptical structuring elements [60], and salience adaptive structuring elements (Paper IV), is presented in Paper VIII. In Paper VIII,
among the other experiments, we examine shapes of adaptive structuring elements of the four aforementioned methods in some typical situations in the image as well as their behaviour when noise is added to the
image (see Paper VIII, Figure 2). As opposed to the traditional morphological operators and the ones compared to in Paper VIII, morphological operators with salience adaptive structuring elements process same
size objects differently depending on their contrast. For example, the
adaptive erosion shrinks dark objects more than light ones, as shown in
Figure 4.5 (c) and (d).
The computation cost of the salience adaptive structuring elements is
relatively high O(N r 2 log r 2 ), where N is the number of pixels in the image and r is the radius of salience adaptive structuring elements. This is
due to calculation of path-based distances for every point in the image.
Therefore, in Paper V, we propose adaptive structuring elements with
a predefined shape, and for which the size is adjusted by the salience
map SM. These structuring elements can be computed in linear time
with respect to the number of pixels in the image. Our method is based
on the geometric relations between the spatial position of the points
and their respective values in the salience map. More precisely, we use
local minima and maxima in the salience map SM to propose two types
of adaptive structuring elements. One type of adaptive structuring elements preserves strong edges better than weak ones, while the other
type better preserves weak edges in the image. More details about this
method can be found in Paper V, Section 3. For instance, the results of
55
(a)
(b)
Figure 4.7. Salience-based parabolic structuring functions for 1D function. (a)
Two structuring functions centered at edge points. (b) Two structuring functions where one is centered at the edge and one is close to the edge.
morphological operators with these structuring elements are depicted
in Figure 4.6.
In the two methods for adaptive structuring elements that are briefly
described above (more details can be found in Paper IV and Paper V),
the size of structuring elements decreases as the salience map SM increases and vice versa. In paper VI, we propose salience-based parabolic
structuring functions that are larger where the salience map SM is smaller, i.e., structuring functions are larger in points of weak edges than in
points with strong edges (Figure 4.7 (a)). Formally, we define a family of salience-based parabolic structuring functions centered at point
x ∈ D as
sx (y) = − α([SM(f )](x), [SM(f )](y)) + β[SM(f )](x)kx − yk2 , y ∈ D,
(4.14)
where α([SM(f )](x), [SM(f )](y)) ≥ 0 is a monotonically non-decreasing
function, β ≥ 0 is a constant. In addition, these structuring functions
are not symmetric and they decrease faster at the side closer to the
higher salience (Figure 4.7 (b)). In other words, the parabola sx3 (y)
centered in point x3 is skewed away from the edges in the input image
f.
4.5 Applications
Classical mathematical morphology has been used to solve various problems in image analysis, such as image filtering and image segmentation.
A detailed study of various real world applications of mathematical morphology can be found in the book edited by Najman and Talbot [74].
56
(a)
(b)
(c)
(d)
Figure 4.8. Isolation of the text in a historical document. (a) Color image of
a historical document; (b) Inverted gray-level image f ; (c) Adaptive erosion
ε(f ); (d) Morphological gradient G(f ), i.e., the difference between adaptive
dilation δ(f ) and adaptive erosion ε(f ).
Adaptive mathematical morphology, similarly to the classical one,
found its use in certain tasks, especially for detecting and enhancing
elongated structures in the image. For instance, Morard et al. [72]
used their region growing structuring elements to detect cracks. For
the same application, adaptive elliptical structuring elements were used
by Landström and Thurley [60]. Similarly, adaptive dilation with structuring elements derived from distance functions based on the structure
tensor were used for closing contours and curves [66]. Furthermore,
Tankyevych et al. [108] used their adaptive morphological filters based
on the Hessian for enhancing vessels in medical images.
The usefulness of the general adaptive neighbourhoods was shown
for noise reduction and image segmentation [34]. Moreover, Gonzales
et al. [44] used adaptive morphological operators for pixel classification. The descriptor of each pixel is formed by a concatenation of the
original image and successive adaptive erosions and dilations of increasing size.
In Paper V, we also present the usefulness of salience-based linear
adaptive structuring elements for problems of text recognition. Morphological operators with these structuring elements are used to detect
57
the text in a historical document, where the text on the back side of the
paper is highly visible on the front side (see Figure 4.8 (a) and (b)). We
construct adaptive structuring elements that preserve weak edges (see
Paper V, Section 3 for the more details). These structuring elements
are larger where the text is, and smaller in the background. Therefore, adaptive morphological operators will affect mostly letters on the
front page, (see Figure 4.8 (c) for the adaptive erosion). Note that, for
this application, we used a scaled salience map SM(f ) that is obtained
by taking NMS(f )/5 of the input image, which reduces the impact of
strong edges.
Image Regularization
A mathematical problem is well-posed (in the sense of Hadamard) if the
three following properties are satisfied [94]:
(i) The solution exists.
(ii) The solution is unique.
(iii) The solution is stable, in the sense that small perturbations in the
equation only lead to small perturbations in the solution.
Otherwise, if one of the requirements (i)-(iii) is not satisfied the problem is ill-posed.
Typical examples for ill-posed problems are inverse problems. In general, inverse problems refer to problems used to reconstruct data that
are corrupted or processed. The inverse mapping is usually not accessible for real world problems, and inverse problems require regularization, which refers to a process of introducing additional information in
order to solve an ill-posed problem.
In image analysis, inverse problems can be formulated by
f = Ag + b,
(4.15)
where A ∈ Rn×n is a linear operator, f ∈ Rn is the input image and
g ∈ Rn is the desirable (reconstructed) image and b ∈ Rn is noise.
One way to find an optimal approximation of g is to consider the
following regularization problem
1
2
∗
kf − Agk + λJ (g) ,
(4.16)
g = arg min
2
g∈Rn
where J (g) is a regularization term and λ is a parameter used for balancing between the data and the regularization term. The regularization term J (g) usually contains information about the edges in the image, and it depends on the image derivatives. For instance, the Tikhonov
regularization term is defined by L2 norm, i.e., J (g) = kgk, while the
58
total variation regularization is characterized by the gradient [89]
J (g) =
n
X
|∇g(xi )|.
(4.17)
i=1
Several regularization techniques utilize morphological operators in
the regularization term, and this regularization is referred to as morphological regularization. For instance, Purkait and Chanda [77] proposed
multi-scaled morphological regularization defined as
J (g) =
N
X
(φkB (g) − γkB (g)),
(4.18)
k=1
where φkB and γkB are morphological closing and opening with disks
of increasing size k as structuring elements. The same authors recently
presented a morphological regularization as [78]
J (g) = φ(g) − γ(g),
(4.19)
where φ and γ are adaptive morphological closing and opening, respectively.
In Paper VII, we propose a new framework for morphological image
regularization using adaptive structuring functions. Our approach is
based on the Lasry–Lions approximations that rely on infimal-supremal
convolution formulas, and provide Lipschitz regularization of non-smooth
functions [61]. The approximations belong to the class of functions
C 1,1 , which means that the approximations are continuously differentiable with Lipschitz continuous gradient (see Appendix for more definitions).
Let f : D ⊂ Rn → R ∪ {+∞} be a lower semi-continuous, convex
and bounded function from below. Lasry and Lions [61] defined the
two following approximations of a function f as
_ ^ 1
1
µ
2
2
f λ (x) =
f (y) +
d(z, y) −
d(z, x) , x ∈ D, (4.20)
2λ
2µ
z∈D y∈D
and
µ
f λ (x)
=
^ _ z∈D y∈D
1
1
2
2
f (y) −
d(z, y) +
d(z, x) , x ∈ D.
2λ
2µ
(4.21)
Following definitions (4.20) and (4.21), we have
µ
f µλ ≤ f ≤ f λ .
(4.22)
µ
Moreover, the approximations f µλ , f λ ∈ C 1,1 , and they converge pointwise to f , when 0 < µ < λ and λ → 0, µ → 0.
59
Note that, if λ = µ then approximations (4.20) and (4.21) correspond to the morphological opening and closing of a function f with the
quadratic structuring function sx (y) = −d(x, y)2 , respectively. Since the
requirement for the Lipschitz regularization is 0 < µ < λ, then we will
refer to the approximations (4.20) and (4.21) as pseudo-opening and
pseudo-closing. Therefore, the above discussion implies that the Lasry–
Lions regularization of non-smooth functions is a pseudo-morphological
operator of the initial function using quadratic structuring elements.
This type of regularization is further extended to non-convex functions, and this work is presented by Attouch and Azé [7]. They presented an equivalence of Lasry–Lions approximations without imposing
the convexity constraint on a function f nor the boundedness. Instead,
they assume that the function is quadratically minorized, i.e., there exists c ≥ 0 such that
c
f (x) ≥ − (1 + kxk2 ), x ∈ D.
2
(4.23)
Then, for all 0 < µ < λ < 1/c the function f µλ ∈ C 1,1 and f µλ → f
converges to f , when µ → 0 and λ → 0. Similar statement is valid for
µ
the approximation f λ [7].
In the above discussion, all approximations are given for the fixed
quadratic structuring functions sx (y) = −d(x, y)2 , x, y ∈ D. It is also
possible to use structuring functions that are different from the quadratic
ones, and structuring functions that depend on the position in the domain D [61]. Furthermore, Cepedello-Boiso [28] developed this theory
for functions in Banach spaces and suggested that structuring functions
with the following properties can be used to obtain the Lipschitz regularization:
(i) sx (x) = 0,
(ii) sx (y) = sy (x),
(iii) sx (y) is Lipschitz continuous on bounded sets.
(iv) sx (y) → −∞, when y → ∞.
Note that there is an indefinite number of possible combinations how
to define the structuring function and its finite support. In mathematical morphology, parabolic structuring functions have been used for several reasons. For instance, morphology with parabolic structuring functions is the counterpart of the Gaussian convolution kernel, because it
is the only structuring element that is rotationally invariant and separable [111]. In Paper VII, we define adaptive structuring functions as
sx (y) = −d(x, y)p(x) ,
x ∈ D,
(4.24)
where the slope p(x) > 1 depends on the image content. These structuring functions satisfy the above properties (i)-(iv). We define p(x)
60
(a)
(b)
(c)
(d)
Figure 4.9. Morphological regularization using a (pseudo openclose + pseudo
closeopen)/2 filter using morphological amoeba as the finite support for structuring functions, and regularization parameters λ = 1, µ = 0.5. (a) Original
image; (b) Image corrupted by 20% random impulse noise; (c) Regularization
using quadratic structuring function for all points in the image. (d) Regularization using adaptive structuring functions.
as an increasing function of the salience map SM(f ), i.e., the slope of
structuring functions is higher in points with larger salience.
In Figure 4.9, we illustrate the described method using the following
function to determine the slope of adaptive structuring functions
p(x) = 1 + 3
[SM(f )](x)
.
max(SM(f ))
(4.25)
For the calculations, we use adaptive finite support for structuring functions, computed by morphological amoebas. The regularization with
adaptive structuring functions produces more smoothing than the corresponding regularization using quadratic structuring functions for every point in the image. Moreover, finite support of structuring functions
has significant influence on the morphological regularization presented
here (Paper VII).
61
5. Conclusions and Future Work
This chapter concludes the thesis and presents some directions for future work.
5.1 Contributions
In this thesis, we have investigated distance functions and their usefulness for several problems within image analysis. The major contributions presented in this thesis are:
• We have proposed a new distance function between sets of points,
called the Complement weighted sum of minimal distances, and
investigated its properties in Zn . The applicability of this distance
function has been presented to image registration. In addition
to extensive evaluation with synthetic images, we have shown the
usefulness of this distance function for several real world problems
(Paper I and Paper II).
• We have proposed two different ways of extending the Complement weighted sum of minimal distances to fuzzy sets. We have
shown that these distance functions can be a useful tool for classifying digital fuzzy objects (Paper III).
• We have proposed adaptive structuring elements based on the
salience map computed from the strength of the edges in the image. We have introduced two different types of structuring elements:
(i) The shape of a structuring element is predefined, and its size
is determined from the salience map (Paper V).
(ii) Both the shape and size of a structuring element are dependent on the salience map (Paper IV).
• We have proposed parabolic adaptive structuring functions that
are based on the salience map (Paper VI). This method is a generalization of the salience adaptive structuring elements proposed
in Paper IV.
• We have proposed a new framework for morphological image regularization using adaptive mathematical morphology (Paper VII).
The methods proposed in Paper IV, Paper V and Paper VI can be
used for this type of image regularization.
63
• We presented an overview of adaptive mathematical morphology,
which will be published in a journal special issue on mathematical
morphology. There, we have presented an application-oriented
study of various methods for adaptive structuring elements (Paper
VIII).
• We have presented a unified framework for proper definition of
adaptive morphological operators, such that adaptive morphological erosion and dilation form an adjunction (Paper VIII).
In summary, we have proposed tools that can be used for different
problems in image analysis. The distance functions, we have proposed,
can be used for different tasks related to comparing various shapes in
images. In addition, we have proposed salience-based mathematical
morphology that can be used to distinguish and modify shapes with
respect to their contrast and not just size.
5.2 Future Work
The methods presented in this thesis can be further improved. Some of
the possible future research topics related to the work presented in this
thesis are presented here.
Set distances
Like other distance functions between sets of points, the Complement
weighted sum of minimal distances can be used for various applications.
For instance, it can be used for evaluating different segmentation methods, which is a problem that often appears in image analysis. Some initial results clearly show this potential, and this distance function might
be a powerful tool for other image analysis problems that include a
comparison of shapes.
The Gromov–Hausdorff distance has been used for shape comparison [24, 71], and this distance function measures how similar shapes
are, where shapes are considered as metric spaces. Similarly to the
Hausdorff distance, the Complement weighted sum of minimal distances
can be extended to this framework.
We have shown that using more membership levels of fuzzy sets does
not necessarily lead to improving the performance of distance functions.
Hence, it would be valuable to determine how the number of membership levels of fuzzy sets influences a particular distance function.
Salience adaptive structuring elements
We have only used the magnitude of the edges in the image to compute the salience map SM thus far. It would be interesting to construct
64
similar salience maps using other attributes of the edges such as curvature or length. Moreover, apart from the salience map computed by the
salience distance transform, other salience maps could be considered
when constructing adaptive structuring elements.
The salience map SM can be modified by scaling attributes of the
edges in the image. It would be valuable to further study this salience
map, especially when considering a specific application.
An extension of the salience distance transform has been recently
presented by Lagerstrom and Buckley [59]. Their method is refer to as
attribute weighted distance transform, and it is dependent on different
image attributes and not only edges. It might be valuable to compute
adaptive structuring elements from the salience map that is computed
using an attribute weighted distance transform.
An extension of mathematical morphology to color images is a challenging task, since it depends on ordering vectors [1, 6]. It would
be interesting to further extend the salience adaptive structuring elements to color images. Some initial results (not yet published) show
that adaptive structuring elements computed from a salience map for
color images might have better properties than other similar methods
of adaptive structuring elements for color images [36, 62].
There might be a connection between morphological operators with
salience adaptive structuring elements and the mean curvature equation. This should be explored.
Adaptive mathematical morphology
Adaptive morphological operators are not well-known to the wider image analysis community. This might be due to a relatively high computational cost required to compute adaptive structuring elements. Hence,
efficient algorithms for computing such structuring elements are required. Some of our initial results show that using GPU implementations, at least for non-local structuring elements, lead to a significant
speed-up [48].
The theory of classical morphological operators is very rich. Possibly,
most of the theory remains valid when using adaptive structuring elements. For instance, it would be interesting to consider definitions of
scale-spaces and a granulometry with adaptive structuring elements as
well as other morphological operators.
Morphological regularization based on Lasry–Lions approximations
is a very elegant method for image regularization. Our initial study
presented in Paper VII gives a framework for morphological image regularization using adaptive mathematical morphology. Further studies
will focus on finding appropriate adaptive structuring functions for particular applications.
65
Most of the methods for adaptive structuring elements can be considered as special cases of the recently introduced mathematical morphology on Riemannian manifolds [4]. This approach seems to be a
prominent future research direction when deriving adaptive structuring
elements. In addition, it might be useful to define adaptive mathematical morphology for other image representations, such as orientation scores [43] and Poincaré upper-half plane [3], two frameworks for
which classical morphological operators are already defined.
The current approach in adaptive mathematical morphology is to define adaptive structuring elements for all points in the image. Even
though this seems a natural way to define adaptive structuring elements, not all points in the image are equally important and most of the
points in the image could be processed with the same rigid structuring
element. Therefore, it seems reasonable to derive adaptive structuring
element only for some specific points in the image, possibly the points
that are more important according to a predefined criterion.
5.3 Concluding Remarks and Perspectives
Because many various distance functions exist in the literature, it is not
always easy to select an appropriate distance function for a particular
application. Furthermore, using existing distance functions might not
always be the best option, when considering a particular problem, and
even small modifications of the existing distance functions can significantly improve the performance of the distance. Therefore, it might be
better to adapt existing distance functions to a particular problem or
propose new distance functions than use the old existing ones.
Mathematical morphology found its use in a number of different applications including image filtering and segmentation. Although the
need for adaptive mathematical morphology is obvious, its future probably depends on the applicability of adaptive morphological operators
to real world problems.
66
6. Brief Summaries of the Included Papers
This chapter presents a brief summaries of the papers included in this
thesis.
Paper I:
Distance functions between sets of points are analysed, and their applicability to image registration problems is studied. The notion of the
complement distance, as a distance function between complements of
two sets, is introduced. An empirical evaluation of monotonicity of distance functions with respect to translations and rotations of 2D binary
objects for different noise conditions is given. It is concluded that distance functions based on the contribution of all points from both sets
have monotonic behaviour more frequent than Hausdorff-like distance
functions, which are based on the maximum of point-to-set distance
values.
Paper II
A novel distance function between sets of points, called the Complement weighted sum of minimal distances, is proposed. For this distance
function, the contribution of each point is weighted according to the
position within the set that it belongs to. An extensive study on the
usefulness of the new distance function for the task of shape registration is performed. Different distance functions between sets of points
are compared, and it is concluded that the Complement weighted sum
of minimal distances has the best performance overall. In addition, its
applicability to two real problems is given: for the recognition of handwritten text characters and for the multi-modal 2D-3D image registration of bone implants in the surrounding tissues.
Paper III
The Complement weighted sum of minimal distances is extended to the
case of fuzzy sets, using two different approaches. One extension is
based on the α−cut decomposition of fuzzy sets, and the second approach is based on the point-to-set distance for fuzzy sets. The monotonicity property of the two novel distance functions with respect to
67
increasing translations and rotations of digital fuzzy objects is analysed,
as well as the usefulness of those distance functions for the recognition of different crisp and fuzzy digital objects. It is shown that the use
of the proposed distance functions between fuzzy sets for classification
of fuzzy objects leads to improved performance, compared to a corresponding classification of binary objects at the same resolution using
corresponding distance functions between binary objects.
Paper IV
A method for the construction of salience adaptive structuring elements
that locally adjust their shape and size according to the salience of the
edges in the image is proposed. Salience adaptive structuring elements
adapt differently to different contrasts of same-size objects, and adaptive morphological operators process these objects differently. The comparison with morphological amoebas is given, and it is concluded that
the salience adaptive structuring elements have a more compact shape,
and are less sensitive to noise than the morphological amoebas.
Paper V
The computational cost for calculating adaptive structuring elements is
relatively high, and often higher than linear with respect to the number
of pixels in the image. Therefore, structuring elements with predefined
shape and adaptive size are proposed. The size of adaptive structuring
elements is determined by the salience of the edges in the image, and
they can be computed in linear time. A visual comparison between
the new adaptive structuring elements and morphological amoebas is
shown. The applicability of the new adaptive morphological operators
is demonstrated for isolating text in a historical document.
Paper VI
Parabolic structuring functions based on the salience of the edges in
the image are introduced. They are not necessarily symmetric with
respect to the origin, and these adaptive structuring functions can be
considered as a generalization of the salience adaptive structuring elements introduced in Paper IV. Furthermore, morphological operators
with adaptive flat structuring elements obtained by thresholding the
salience-based parabolic structuring functions are also presented. In addition, the proper way of computing adjunct morphological operators is
discussed.
68
Paper VII
The applicability of adaptive mathematical morphology to the task of
image regularization is studied. The underlying theory is based on
Lasry–Lions regularization of non-smooth functions. The Lasry–Lions
regularization is Lipschitz continuous, and it corresponds to morphological or pseudo-morphological operators with adaptive structuring functions. An empirical evaluation on using different regularization parameters is presented and different adaptive structuring functions are considered. It is concluded that the finite support of structuring functions
has significant impact on this regularization process.
Paper VIII
An overview of adaptive mathematical morphology is presented. Different aspects of adaptivity in mathematical morphology are considered,
and the focus is given to different methods for constructing adaptive
structuring elements. An application-oriented study of four methods
for adaptive structuring elements is presented, where advantages and
disadvantages of each method are discussed. The proper definitions
of adaptive morphological operators for both structuring elements and
structuring functions are given. Furthermore, various possible directions how the field of adaptive mathematical morphology might evolve
in future are discussed.
69
Appendix
Norm
Given a vector space V , a norm on V is a function k · k : V → R that
satisfies the following properties, for all x, y ∈ V and α ∈ C:
1. kxk ≥ 0,
2. kxk = 0 if and only if x = 0,
3. kαxk = |α|kxk,
4. kx + yk ≤ kxk + kyk.
Lipschitz continuity
Let (X, dX ) and (Y, dY ) be two metric spaces. A function f : X → Y is
Lipschitz continuous if there exists a constant λ > 0 such that
dY (f (x1), f (x2 )) ≤ λdX (x1 , x2 ),
for all x1 , x2 ∈ X. A constant λ is referred to as Lipschitz constant.
Banach space
A metric space (X, d) is a Banach space if every Cauchy sequence has
limit in X.
Hilbert space
An inner product h·, ·i : H × H → C satisfies the following properties:
1. hx, yi ≥ 0, x ∈ H,
2. hx, xi = 0 if an only if x = 0, x ∈ H,
3. hy, xi = hx, yi, x, y ∈ H,
4. hax1 + bx2 , yi = ahx1 , yi + bhx2 , yi, x1 , x2 ∈ H, a, b ∈ C,
5. hx, ay1 + by2 i = āhx, y1 i + b̄hx, y2 i, y1 , y2 ∈ H, a, b ∈ C.
A Hilbert space is a vector space H with an inner product hx, yi such
that the norm defined by
p
kx − yk = hx − y, x − yi.
A distance d between two points x, y ∈ H can be defined in terms of
the norm by
p
d(x, y) = kx − yk = hx − y, x − yi.
71
Acknowledgements
First of all, I am very grateful for these five years as a graduate student
at the Uppsala University. My PhD position was funded by the Graduate
School in Mathematics and Computing and I am grateful for this financial support. I would like to thank all the people at our centre/division.
Special thanks to the following people who contributed to the thesis in
one way or the other:
• Gunilla Borgefors, my main supervisor, for giving me the opportunity to do research in image analysis, for the support during these
years, for having confidence in me and for encouraging me to work
on adaptive mathematical morphology.
• Cris Luengo, my assistant supervisor, for encouraging me to develop my own ideas and for great discussions that we had on research, mathematical morphology and life in general. It was a
great pleasure learning from you!
• Joakim Lindblad and Nataša Sladoje, my assistant supervisors, for
introducing me to image analysis and for encouraging me to apply
for this PhD position, and for their valuable support in the first two
years of my PhD studies.
• Jesús Angulo, for being such a good host during my visit at the
Centre for Mathematical Morphology, Fontainebleau, France. For
sharing a lot of interesting ideas on mathematical morphology and
research in general, and for our close collaboration on Paper VII.
• Anders Landström and Matthew Thurley, for our excellent collaboration during these years. I had the great please working with
Anders on Paper VIII.
• Researchers in the mathematical morphology community: Sébastien
Lefèvre, Johan Debayle, Santiago Velasco-Forero and Victor GonzálesCastro, for our collaborations and for your influence on my work.
• Ewert Bengtsson, Ingela Nyström, Lena Nordström, and Olle Eriksson for taking a good care of our division and helping me with all
administrative issues.
• The more permanent staff: Ingrid Carlbom, Ida-Maria Sintorn,
Carolina Wälhby, Robin Strand, Anders Brun and Filip Malmberg,
for all ideas and discussions about research and life in general.
• My officemates during all these years, Erik Wernersson, Catherine Östlund, Jimmy Azar, Fredrik Wahlberg, Kristina Lidayová and
Olle Eriksson for all nice discussions and fun we had.
73
• Hamid Sarve and Amin Allalou, also known as Braintrust or BT,
for such a great fun we had all these years, for all discussions we
had and for everything.
• Milan Gavrilović and Khalid Niazi for late night working hours and
for fruitful discussions on image analysis and life in general.
• Patrik Malm for being a really good friend and my teacher into the
Swedish culture, from surströmming to innebandy.
• Gustaf Kylberg, for helping me whenever I had a problem with
plots and graphs.
• Azadeh Fakhrzadeh, for being a great friend, for all fun we had
and for our dreams about the future.
• Bettina Selig, for friendship, fun, support, and for always being
around.
• Lennart Svenson for being a friend and a great lab partner. Also
for inviting me to his beautiful home in Västerås.
• Pontus Olsson, for interesting discussions about the curve of life
and its derivatives.
• Elisabeth Linnér, for knitting during the seminars and making me
happier.
• Alexandra Pacureanu and Christophe Avenel, for bringing France
to our division.
• The Nysjö brothers, Johan and Fredrik, for being similar and different at the same time.
• Andreas Kårsnäs and Fei Liu, for being good friends despite spending only a few days in Uppsala.
• More recent PhD students at our division: Omer Ishaq, Kaylan
Ram, Sajith Sadanandan Kecheril, for the nice discussions we had.
• My closest Uppsala friends: Katja, Markus, Abhi, Majid, Else, Juan,
Camille, Vera, Camij, Pavol, Jonathan, Per, Anders, Fredrik and
others, for social activities that we had all these years.
• My friends from volleyball: Andrea, Eliza, Joanna, Heidi, Gurdip,
Marco (el capitano), Justin, Frank, Alex and Dimitris, for having
such a great time on the volleyball court. And for being officially
the best team in Campus 1477!
• Many thanks to my closest friends: Ceca, Sloba, Mića, Mira, Jens,
Vuk, Lidia, Djudja, Ćeba, Dejan (VC), Kosta-kumić, and others, for
your friendship.
• My dear colleagues from the Faculty of Technical Sciences, University of Novi Sad, Serbia, for your support.
• Gunilla Borgefors, Cris Luengo, Nataša Sladoje, Robin Strand, Hamid
Sarve and Johan Nysjö, for proofreading and commenting on drafts
of this thesis.
74
• My extended family for all support I had during these years. For
all SMS messages, Skype calls and for awaiting me at the Nikola
Tesla International airport in Belgrade and making my life easier.
• The end of PhD studies can be seen as the end of an educational
journey that in my case started in 1988 in Klek, a small village
in Serbia. My mother Stana and my father Žarko have been my
constant support during all these years. Mama i tata, hvala vam!
Najbolji ste! Volim vas!
• My finance, Iva Lučić, for bringing love into my life. Words cannot
describe how lucky I am to have you in my life. Thank you for
your unconditional love, for your time and support, and all the
moments we shared. I look forward to our lifelong journey. I love
you! Volim te najviše na svetu, ljubavi moja! ♥
75
Summary in Swedish
Denna avhandling behandlar avståndstransformer och dessas användning inom datoriserad bildanalys. Speciellt behandlas två delområden:
avståndstransformer mellan punktmängder och deras tillämpning på
bildregistreringsproblem; och användning av avståndstransformer för
att definiera adaptiva strukturelement och strukturfunktioner som i sin
tur används för att konstruera adaptiva morfologiska operatorer.
Ett antal olika avståndsfunktioner har använts för ett antal olika problemlösningar. Trots att den vetenskapliga litteraturen beskriver många
olika avståndsfunktioner är det inte lätt att välja den lämpligaste av dem
för att lösa ett specifikt problem. Därför är det intressant att studera
olika funktioner och beskriva deras egenskaper, liksom att introdusera
nya avståndsfunktioner.
Här studerar vi avståndsfunktioner mellan mängder och vi utvärderar
deras teoretiska egenskaper och deras användbarhet för bildregistreringsproblem. Speciellt undersöker vi monotonitet hos avståndsfunktionerna under translation och rotation samt bruskänslighet. Förutom
existerande avståndsfunktioner mellan mängder, där tidsåtgången för
beräkningen är en linjär funktion av antalet pixlar i bilden, föreslår
vi en ny avståndsfunktion, som vi kallar “komplementet till den viktade summan av minimala avstånd”. Denna avståndsfunktion mellan
mängder lägger högre vikt vid punkter som ligger djupare inne i objektet och lägre vikt vid punkter som är närmare objektets kant. Detta
överensstämmer väl med situationen i olika tillämpningar, där det oftast
är punkter nära objektens kanter som mest påverkas av brus.
Vi visar att komplementet till den viktade summan av minimala avstånd är överlägen vid bildregistrering och matchning av 2D-objekt när
vi jämför den med andra liknande avståndsfunktioner. Vi visar dess
användbarhet för igenkänning av handskrivna symboler och för multimodal registrering från 2D till 3D: det två dimensionella histologiska
snittet genom ett benimplantat med kringliggande vävnad registreras
till motsvarande läge i en tredimensionell SRµCT-volym av samma implantat. Dessutom utvidgar vi komplementet till den viktade summan
av minimala avstånd till ett avstånd mellan oskarpa mängder och vi
visar hur användbart detta är vid avståndsbaserad klassifikation.
Den andra delen av avhandlingen fokuserar på avståndstransformer
för adaptiv matematisk morfologi. Matematisk morfologi utgör ett kraftfullt koncept för icke-linjär bildbehandling, och skapar därmed värde77
fulla verktyg för många uppgifter, såsom bildfiltrering, bildsegmentering, formjämförelse, mm. Det vanligaste sättet att definiera morfologiska operatorer är att använda begreppet strukturelement. Strukturelement är vanligtvis små punktmängder som används för att undersöka
bilder. Resultatet av den morfologiska operationen beror på interaktionen mellan bilden och strukturelementet.
Adaptiv matematisk morfologi har nyligen blivit ett populärt forskningsområde inom matematisk morfologi. Den underliggande idén är
att definiera morfologiska operatorer som inte behandlar alla punkter i bilden på samma sätt, utan tar hänsyn till hur viktig varje punkt
är. Därför definieras adaptiva morfologiska operatorer genom adaptiva strukturelement som anpassas till lokala förhållanden i bilden. För
varje punkt i bilden kan det adaptiva strukturelementet beräknas med
hjälp av olika bildegenskaper, till exempel likhet mellan grånivåerna för
punktgrannar, gradienter och orientering.
Vi skapar adaptiva strukturelement som baseras på en signifikanskarta som i sin tur baseras på styrkan hos kanterna i bilden, det vill
säga på hur viktig varje kantpunkt är. En sådan signifikanskarta skapas genom att beräkna en avståndstransform som tar hänsyn till både
avstånd mellan punkter och styrkan hos bildens kanter. Egenskaperna
sprids över bilden med hyvlingsalgoritmen.
Två olika typer av adaptiva strukturelement har definierats. I det
första fallet är strukturelements form förbestämd och endast dess storlek bestäms av signifikanskartan. I det andra fallet beror både strukturelementets form och storlek på signifikanskartan. Dessutom definierar vi adaptiva strukturfunktioner, som är strukturelement där varje
punkt har en vikt mellan minus oändligheten och noll. Dessa adaptiva strukturfunktioner baseras också på signifikanskartan och utgör en
generalisering av de adaptiva strukturfunktioner vi först introducerade.
De nya adaptiva strukturfunktionerna är icke-symmetriska paraboliska
funktioner.
Den här avhandlingen visar även på användbarheten av adaptiv matematisk morfologi för bildregularisering. Samspelet mellan adaptiv
matematisk morfologi och Lipschitz-regularisering av Lasry–Lions-typ
för icke-släta funktioner skapar ett elegant verktyg för morfologisk bildregularisering. Med andra ord använder denna typ av bildregularisering adaptiva morfologiska operatorer fr att skapa Lipschitz-regularisering
av bilder som innehåller brus. Avhandlingen innehåller även en översikt av adaptiv matematisk morfologi, tillsammans med en jämförelse av
olika metoder för att skapa adaptiva strukturelement och avslutas med
olika möjliga framtida forskningsinriktningar inom adaptiv matematisk
morfologi.
78
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