Deep Structure, Singularities, and Computer Vision DSSCV Deliverable

Deep Structure, Singularities, and Computer Vision DSSCV Deliverable
Deep Structure, Singularities, and Computer Vision
DSSCV
IST-2001-35443
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21
Report on assessment of MSSTs for 3D Matching Application
K. Somchaipeng, J. Sporring, S. Kreiborg, and P. Johansen
3D-Lab, School of Dentistry, Dept. of Pediatric Dentistry, University of Copenhagen
Public
20 September 2004
Report on Assessment of MSSTs for 3D Matching Application
K. Somchaipeng1,2 , J. Sporring2 , S. Kreiborg1 , and P. Johansen2
1 3D-Lab,
School of Dentistry, Dept. of Pediatric Dentistry, University of Copenhagen Nørre Alle 20,
DK-2200 Copenhagen N, Denmark
2 Dept.
of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100,
Copenhagen N, Denmark
Abstract
We consider images as manifolds embedded in a hybrid high dimensional space of coordinates
and features. Images are partitioned into segments based on the energy functional and mathematical landmarks. The nesting of image segments in scale-space is used to construct image hierarchies called Multi-Scale Singularity Trees (MSSTs). Two kinds of mathematical landmarks are
proposed namely extremal points and saddle points.
We study the stability of the Extrema-Based MSSTs extracted from a series of smoothly
changed generated images. The observed MSST transitions can be categorized into three categories: changes of positions, changes of relations, and changes of connections. The stability
comparison between Extrema-Based MSSTs and Saddle-Based MSSTs is also briefly presented.
We point out the equivalence between MSSTs and Multi-Scale Singularity Strings (MSSSs)
and propose two sets of edit operations on MSSSs, one for Extrema-Based MSSSs and one for
Saddle-Based MSSSs. Simple examples of editing MSSSs are also presented.
We describe a medical image database of volumetric CT scans of the craniofacial area and
manually segmented teeth. The database is aimed at evaluating the image matching algorithm
based on MSSTs.
1
Gaussian Scale-Space
The N + 1 dimensional Gaussian scale-space, L : RN +1 → R, of an N dimensional image, I : RN →
R, is an ordered stack of images, where each image is a blurred version of the former [7, 20, 9]. The
blurring is performed according to the diffusion equation,
∂t L = ∇2 L,
(1)
where ∂t L is the first partial-derivative of the image in the scale direction t, and ∇2 is the Laplacian
operator, which in three dimensions reads ∂x2 + ∂y2 + ∂z2 .
The Gaussian kernel is the Green’s function of the heat diffusion equation, i.e.
L(·; t) = I(·) ⊗ g(·; t),
g(x; t) =
1
T
e−x x/(4t) ,
N/2
(4πt)
(2)
(3)
where L(·, t) is the image at scale t, I(·) is the original image, ⊗ is the convolution operator, g(·; t) is
the Gaussian kernel at scale t, N is the image dimensionality, and t = σ 2 /2, using σ as the standard
2
deviation of the Gaussian kernel. The Gaussian scale-space is henceforth called the scale-space in
this article.
The information in scale-space is logarithmically degraded, the scale-parameter is therefore often
sampled exponentially using σ = σ0 eT . Since differentiation commutes with convolution and the
Gaussian kernel is infinitely differentiable, differentiation of images in scale-spaces is conveniently
computed,
∂xn L(·; t) = ∂xn (I(·) ⊗ g(·; t)) = I(·) ⊗ ∂xn g(·; t).
(4)
Alternative implementations of the scale-space are multiplication in the Fourier Domain, finite differencing schemes for solving the heat diffusion equation, additive operator splitting[19], and recursive
implementation[6, 18].
Each method has its advantages and disadvantages, we prefer the spatial convolution, since it is
guaranteed not to introduce spurious extrema in homogeneous regions at low scales. Typical border
conditions are Dirichlet, Cyclic repetition, and Neumann boundaries. We use Dirichlet boundaries,
where the image is extended with zero values in all directions according to the size of the convolution
kernels.
Although the dimensionality of the constructed scale-space is one higher than the dimensionality
of the original image, critical points, in the image at each scale are always points. A critical point is
e.g. an extremum, ∂x L = ∂y L = 0. Critical points are classified by the eigenvalues of the Hessian
matrix, the matrix of all second derivatives, computed at that point. Critical points with all positive
eigenvalues are minima, critical points with all negative eigenvalues are maxima, and critical points
with a mixture of both negative and positive eigenvalues are saddles.
As we increase the scale parameter, the critical points move smoothly forming critical paths.
Along scale, critical points meet and annihilate or are created. Such events are called catastrophic
events, and the points where they occur are called catastrophe points. The collection of events at all
scales is called the deep structure of the image.
The notion of genericity is used to disregard events that are not likely to occur for typical images,
i.e. generic events are stable under slight perturbation of the image. There are only two types of
generic catastrophe events in scale-space namely pairwise creation events and pairwise annihilation
events[5]. It has further been shown that generic catastrophe events only involves pairs of critical
points where one and only one eigenvalue of the Hessian matrix changes its sign, e.g. the annihilation
of a minimum (+, +, +) and a saddle (+, +, -). A detailed discussion of a robust method for extracting
critical paths and catastrophe points from scale-spaces can be found in [15].
2
Multi-Scale Singularity Trees
There are a few methods for constructing hierarchies from the deep structure of two-dimensional
images proposed in the literatures so far [11, 10, 13]. To the best of our knowledge, an attempt to
construct hierarchies from the deep structure of three-dimensional images is first proposed in [12]
followed by [15]. The latter scheme, which will be extended in this Section, produces rooted ordered
binary trees called Multi-Scale Singularity Trees (MSSTs) with catastrophe points as nodes. It only
considers the linking of the mathematical landmarks which present at the first scale image. Creation
catastrophes are ignored
2.1
Energy Functional and Energy Partitions
Let Ω ⊂ RN be a compact connected domain and define I : Ω → R+ to be an image, ~e ∈ Ω as a
landmark, and ~x ∈ Ω as a point in the domain. Consider a set of continuous functions γ : [0, P ] → Ω
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3
2.2
Building Scale-Space Hierarchies
for which γ(0) = ~e and γ(P ) = ~x, γ ∈ Γ~e~x , where Γ~e~x is the set of all possible paths from a landmark
~e to a point ~x, and γ is parameterized using Euclidean arc-length. The energy E~e (~x) with respect to
an extremum ~e calculated at ~x is defined as
Z Ps
1
∂γ(p) 2
∂I(γ(p)) 2
E~e (x) = inf √
|
| +α |
| dp.
(5)
γ∈Γ~e~x
∂p
∂p
α 0
Considering images as manifolds embedded in a high dimensional space. An N dimensional intensity
image becomes an N dimensional manifold embedded in a hybrid N + 1 dimensional space of coordinates and the features1 , the “space-feature”[8]. For two dimensional images, it is commonly known
as the height plot of the image. The energy defined at a point associated with a landmark can then be
thought of as the minimum of the distances travelling up and down from the landmark to that point.
When the coefficient α is set to infinity, the energy is also known as the path variation, a generalization
of the total variation[3].
Let E ⊂ Ω be the set of all landmarks in the image. An image segment or an energy partition Si
associated with a landmark ~ei ∈ E is defined as the set of all points in the images, where the energy
E~ei (~x) is minimal,
Si = {~x ∈ Ω|E~ei (~x) < E~ei (~x), ∀~ej ∈ E, i 6= j}.
(6)
An approximation of the energy map Mi : Ω → R+ , which defines the energy at every point in the image associated with a landmark ~ei , can be efficiently calculated using the Fast Marching Methods[14].
The tessellation of the image segments obtained depends on the selection of the landmarks and
the energy functional. Mathematical landmarks like extremal points seem to be a natural selection
since they represent physically the image content, significant features in the image contain at least one
such points, and they can be easily and automatically detected. The selection of extremal points as
landmarks leads to Extrema-Based MSSTs.
Another candidate for landmarks are the saddles points, which leads to Saddle-Based MSSTs.
We show later in Section 3. that the transition of Saddle-Based MSSTs are simpler than those of
Extrema-Based MSSTs.
2.2
Building Scale-Space Hierarchies
MSSTs are obtained by linking catastrophe points based on the nesting of image segments in scalespace. Resulting MSSTs are always rooted ordered binary tree. For simplicity, we will begin by
describing the building procedures of Extrema-Based MSSTs and then move to Saddle-Based MSSTs
at the end of the Section.
2.2.1
Extrema-Based MSSTs
Each node of the tree consists of three parts, the leftport, the body, and the rightport. The leftport
represents the image segment that immediately covers the image segment represented by the rightport,
which disappears after the catastrophe event mentioned in the body. Assuming that critical paths
and catastrophe points in the scale-space image are already and correctly extracted, the tree building
algorithm is as follows:
The algorithm starts by setting the root of the tree as BC∞ Elast , where B denotes border of the
image, Ei denotes the last extremum points in scale, and C∞ denotes the virtual catastrophe at scale
infinity where the border and the last extremum points virtually annihilate.
1
In this case the only feature is the intensity or the zeroth jet space. The energy functional can be easily defined for
higher order jet space images, color images, or locally orderless images with scale-space histograms to handle textures[17],
if a metric in the feature space is given.
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2.3
Discussion
At the highest catastrophe point Ctop in scale, calculate an energy map with respect to the catastrophe Ctop , create a node Ecover Ctop Eann , where Ecover is the extremum which its image segment
covers the catastrophe Ctop , and Eann is the extremum that annihilates with a saddle at the catastrophe
Ctop . Link the new created node as the left child of the root if Ecover equals B or as the right child of
the root if Eann equals Elast .
Determining the covering image segment is easy since it is the image segment belonging to the
extremum or the border with minimal energy. We simply choose the extremum or the border that has
the lowest value in the energy map with respect to the catastrophe.
Repeat the same procedure on the next highest catastrophe Cnext to the lowest one but instead of
linking the new node as the right child or the left child of the root, link the new node as the left child
of a node in the tree, which does not have its left child and its leftport equals the leftport of the new
node, or link the new node as the right child of a node in the tree, which does not have its right child
and its rightport equals the leftport of the new node. There will be only one possible linking point
exists. An example of Extrema-Based MSSTs together with the deep structure it represents are shown
in Figure 1(a).
2.2.2
Saddle-Based MSSTs
A similar procedure is applied for constructing Saddle-Based MSSTs. The algorithm starts with setting
the root of the tree as Ctop Stop , where the leftport is set to null, Ctop denotes the highest catastrophe
in scale, and Stop denotes the saddle point that annihilates with an extremum at Ctop . The root will
have only the right child which is Stop Cnext Sann , where Cnext denote the next highest catastrophe
point, and Sann denotes the saddle point that annihilates with an extremum at the catastrophe Cnext .
Go down the scale to the next highest catastrophe and repeat the procedure described in Section 2.2.1. considering saddle points as landmarks instead of extrema. An example of Saddle-Based
MSSTs is shown together with the deep structure it represents in Figure 1(b).
2.3
Discussion
We prefer our tree building method because unlike all other proposed methods, which produce hardlinked trees: only the best linking point is suggested, our method produces soft-linked trees in the
sense that all possible linking points are suggested where linking points with the lowest energy is
selected. The added information is very useful for estimating the costs of transitions.
If the energies associated with a catastrophe point calculates at two different landmarks are slightly
different, it implies that the link could then be easily switched with small perturbation on the image
and the cost of this transition should be small accordingly. On the other hand if the energy difference
is large, it then can be assumed that the link is rather stable and a high cost should be paid for the
transition. The cost of that transition can be estimated proportionally to the difference of the two
energies.
3
Stability of MSSTs
To empirically study the stability of MSSTs, a series of generated images of three stationary and one
moving Gaussian blobs is used in the experiment. All images in the series have the same number of
extrema, i.e. a minimum surrounded by four maxima. Smooth changes of the images are forced by
smoothly changing the parameters used for generating the images. For each test image a scale-space is
computed and the Extrema-Based MSST is constructed. Changes in the constructed MSSTs between
neighbouring images are observed and carefully classified into categories.
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Border
Discussion
Scale
3.1
BC∞ Eb
Ca
Cc
BCd Ef
Eb Ca Ea
Cb
null
null
Eb Cc Ed
Cd
Ea
Sa Eb
Ec
Sb
Ed
Sc Sd Ef
null
null
Ea Cb Ec
null
null
Space
Border
Scale
(a)
Ca Sa
Ca
Sa Cc Sc
Cc
Cb
Sa Cb Sb
null
Cd
Ea
Sa Eb
Ec
Sb
Ed
Sc Sd Ef
null
Sc Cd Sd
null
null
Space
(b)
Figure 1: The deep structure with its corresponding Extrema-Based MSST(a) and the deep structure
with its corresponding Saddle-Based MSST(b)
Three categories of transitions are observed in the experiment, i.e. changes of positions, changes
of connections, and changes of relations. Changes of positions are the movements of the extrema and
catastrophe points, changes of connections swap the connection between two extremum-catastrophe
pairs, and changes of relations are the changes of the nesting of the energy partitions in scales. Interested readers can find more detail on the experiment in [16].
Figure 2. shows the before and after images of the observed three transition categories, with the
critical paths and the linkings superimposed on the images.
3.1
Discussion
In practice, some of the the observed three transition categories might occur at the same time. In
general, changes of positions occur most frequently. Changes of connections and changes of relations
usually accompanied by changes of positions.
Since the process of constructing Saddle-Based MSSTs is only interested in saddle points and
their paths in scale-space, the changes of connections, which are the connections between catastrophe
points and extremal points, do not effect their structures at all. There are then only two kinds of
transitions for Saddle-Based MSSTs: changes of positions and changes of relations. Therefore we
expect Saddle-Based MSSTs to be more stable than Extrema-Based MSSTs.
4
Multi-Scale Singularity Strings
Due to redundancies in the tree structure and the data stored in the nodes of the tree, MSSTs can
be converted to string structures simply by concatenating nodes of the tree in order according to the
Version: August 27, 2014
6
Figure 2: The three observed transitions of Extrema-Based MSSTs shown as images before (left
column) and after (right column) the three categories of transitions: changes of positions (top row),
changes of connections (middle row) and changes of relations (bottom row). The critical paths and
the linkings are superimposed on the images: saddle paths in blue, maximum paths in red, minimum
paths in green, and the linkings in yellow.
Version: August 27, 2014
7
[BC∞ Eb ][Eb Ca Ea ][Eb Cc Ed ][Ea Cb Ec ][BCd Ef ]
(a)
[Ca Sa ][Sa Cc Sc ][Sa Cb Sb ][Sc Cd Sd ]
(b)
Figure 3: The Extrema-Based MSSS(a) and the Saddle-Based MSSS(b) equivalent to their corresponding MSSTs in Figure 1(a) and 1(b).
scales of the catastrophes. The resulting strings are called Multi-Scale Singularity Strings (MSSSs).
The information encoded in the structure of the tree is completely kept in the string. An MSST can be
converted to its equivalent MSSS and vice versa without lose of information.
The Extrema-Based MSSS and the Saddle-Based MSSS equivalent to the Extrema-Based MSST
and the Saddle-Based MSST in Figure 1(a) and 1(b) are shown in Figure 3 (a) and (b), respectively.
5
Matching MSSTs
We would like to solve the problem of image matching by matching their pre-computed MSSTs. The
similarity between two images can be estimated as the minimum total cost of a series of edit operations
that transforms the MSST of one image to the MSST of the other. This problem is commonly known
as the Tree Edit Distance (TED) problem[4].
Because MSSTs can be equivalently represented by their equivalent MSSSs and edit operations
on string are simpler and easier to be defined than those on tree, we choose to solve the problem of
matching MSSTs by matching their equivalent MSSSs. Due to the nature of causality in scale-space
there is a right-to-left dependency between nodes in the MSSSs. Unlike normal character strings,
MSSSs are supposed to be edited from right to left.
In order to avoid insertion operations, which is hard to define, two MSSSs SA and SB are edited
to a common MSSS SC . Given two MSSSs SA and SB , the edit distance edit(SA → SB ) is the sum
of the total cost of a series of edit operations {o1 , o2 , . . . , on } that changes SA to SC and the total cost
of a series of delete operation {d1 , d2 , . . . , dm } that change SB to SC .
edit(SA → SB ) =
n
X
cost(oi ) +
i=1
m
X
cost(dj )
(7)
j=1
The cost of the delete operation can be defined proportionally to the stability measures of that catastrophe points. To make the distance symmetric, the distance between SA and SB , distance(SA , SB ),
is defined as the sum of edit(SA → SB ) and edit(SB → SA ).
distance(SA , SB ) = edit(SA → SB ) + edit(SB → SA )
(8)
In order to be able to define costs for all possible transitions full energy matrices are needed, however
with only energy maps constructed with respect to catastrophe points, as described in the building
process of MSSTs in Section 2.2., we can only obtained values in the lower triangle of the matrix. We
propose to calculate energy maps with respect to landmarks at the first-scale image. This way, full
energy matrices can be obtained.
5.1
Editing Extrema-Based MSSSs
Each node in the Extrema-Based MSSS contains only symbols. The content of each symbol, which is
what actually to be matched, must be stored in the following tables and matrices.
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8
5.1
Editing Extrema-Based MSSSs
1. Extremum Table (E-Table)
The table containing the positions and features of all extrema in the first-scale image.



TE = 

Eo
E1
..
.
En
: position0 , f eatures0
: position1 , f eatures1
..
..
.
.
: positionn , f eaturesn





2. Catastrophe Table (C-Table)
The table containing the positions, features, and stabilities of all catastrophe points in the scalespace image.



TC = 

C1
C2
..
.
:
:
..
.
position1 , scale1 , f eatures1 , stability1
position2 , scale2 , f eatures2 , stability2
..
.





Cn : positionn , scale3 , f eaturesn , stabilityn
3. Extremum-Extremum Energy Matrix (E-E Matrix)
The matrix containing the energy of the minimal path between all extrema in the first-scale
image.



MEE = 

0
E1 E0
..
.
E0 E1
0
..
.
En E0 En E1

· · · E0 En
· · · E1 En 


..
..

.
.
···
0
4. Extremum-Catastrophe Energy Matrix (E-C Matrix)
The matrix containing the energy of the minimal paths between all catastrophe points in the
scale-space image and all extrema in the first-scale image.



MCE = 

C1 E0
C2 E0
..
.
C1 E1
C2 E1
..
.
Cn E0 Cn E1
· · · C1 En
· · · C1 En
..
..
.
.
· · · Cn En





These tables and matrices are part of the Extrema-Based MSSS and are given to the matching algorithm. The cost of each edit operation is derived from these tables and matrices.
5.1.1
Edit Operations on Extrema-Based MSSSs
Five edit operations make the complete set of edit operations on Extrema-Based MSSSs. The five edit
operations are as follows.
1. Extremum-Editing E(Ei → [pos, f eat])
Changing of the position and features of the extrema Ei stored in the E-Table.
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9
5.1
Editing Extrema-Based MSSSs
cost(E(Ei → [pos, f eat])) = we | Ei − [pos, f eat] |
2. Catastrophe-Editing C(Ci → [pos, f eat])
Changing of the position and features of the catastrophe Ci stored in the C-Table. If the ordering
of catastrophe points in scale changes, MSSS must be changed accordingly. Illegal relations, if
exist, must be properly updated.
cost(C(Ci → [pos, f eat])) = wc | Ci − [pos, f eat] |
3. Left-Relabeling L(Elef t → Enew )
Changing of the leftport from Elef t to Enew which corresponds to changing of the nesting
relations from being nested inside Elef t to Enew .
cost(L(Elef t → Enew )) = wl | Eright Enew − Eright Elef t |
4. Right-Relabeling R(Eright → Enew )
Changing of the rightport from Eright to Enew which corresponds to changing of the connection
from Ccat being connected to Eright to Enew . Illegal connections and relations, if exist, must
be properly updated.
cost(R(Eright → Enew )) = wr | Ccat Eright − Ccat Enew |
5. Deletion D(Elef t Ccat Eright → φ))
Deleting a node Elef t Ccat Eright from the MSSS. Illegal relations, if exist, must be properly
updated.
cost(D(Elef t Eright Ccat )) = wd stability(Ccat )
5.1.2
Simple Example
We consider a simple case of editing two Extrema-Based MSSSs SA and SB extracted from two
images IA and IB with two extrema respectively. The extrema in the two images are located at different
positions and have different intensity values. The deep structures of SA and SB are shown in Figure
4(a) and Figure 4(b), respectively.
Let SA be an Extrema-Based MSSS consisting of two nodes: [BC∞ E0 ][E0 C1 E1 ] with
TE =
TC =
E0 : pos = 4
E1 : pos = 1
,
C1 : pos = 2, scale = 2
,
and SB be another Extrema-Based MSSS also consisting of two nodes: [BC∞ E0 ][E0 C1 E1 ] with
TE =
Version: August 27, 2014
E0 : pos = 1
E1 : pos = 4
10
,
Scale
Saddles-Based MSSSs
Scale
5.2
3.0
3.0
C1
2.0
2.0
1.0
0.0
0.0
C1
1.0
1.0
E1
2.0
3.0
S0
Space
4.0
E0
0.0
0.0
1.0
E0
2.0
S0
(a)
3.0
4.0
E1
Space
(b)
Figure 4: The deep structure of SA and SB
TC =
C1 : pos = 3, scale = 3
.
We would like to find a series of edit operations that change SA to SB through a common MSSS SC .
The series of the edit operations that changes SA to SC , where SC is the common MSSS, consists of
two edit operations2
C(C1 → [pos = 3, scale = 3])R(E1 → E0 ) .
The series of delete operation that changes SB to SC is empty and the common string SC equals SB .
The edit cost edit(SA → SB ) is then
edit(SA → SB ) = cost(C(C1 → [pos = 3, scale = 3])) + cost(R(E1 → E0 )) .
The first operation corrects the position and scale of the catastrophe C1 , the second operation changes
the connection of C1 from being connected to E1 to being connected to E0 . Because E0 is no longer
present at the scale of C1 , the leftport E0 is then no longer valid and must be updated to E1 . The SA
after applying the edit operations is [BC∞ E0 ][E1 C1 E0 ] with
TE =
TC =
E0 : pos = 4
E1 : pos = 1
,
C1 : pos = 3, scale = 3
,
which is now equivalent to SB .
5.2
Saddles-Based MSSSs
Similar to Extrema-Based MSSSs, each node of the Saddle-Based MSSS contains only symbols. The
content of each symbol, which is what we actually want to match, are stored in the following tables
and matrices.
2
Assuming that this series of edit operation give the lowest total cost.
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11
5.2
Saddles-Based MSSSs
1. Saddle Table (S-Table)
The table containing the positions and features of all saddles in the first-scale image.



TS = 

S1
S2
..
.
Sn
: position0 , f eatures0
: position1 , f eatures1
..
..
.
.
: positionn , f eaturesn





2. Catastrophe Table (C-Table)
The table containing the positions, features, and stabilities of all catastrophe points in the scalespace image.



TC = 

C1
C2
..
.
:
:
..
.
position1 , scale1 , f eatures1 , stability1
position2 , scale2 , f eatures2 , stability2
..
.





Cn : positionn , scale3 , f eaturesn , stabilityn
3. Saddle-Saddle Energy Matrix (S-S Matrix)
The matrix containing the energy of the minimal paths between all saddles in the first-scale
image.



ESS = 

0
S2 S1
..
.
S1 S2
0
..
.
Sn S1 Sn S2
5.2.1

· · · S1 Sn
· · · S2 Sn 

.. 
..
.
. 
···
0
Edit Operations on Saddles-Based MSSSs
Because the connections between catastrophe points and extrema in scale-space can change but the
connections between saddles and catastrophes are fixed, edit operations on Saddle-Based MSSSs are
simpler. There are only three edit operations needed as follows.
1. Saddle-Catastrophe-Editing SC(Sright → [poss , f eats ], Ccat → [posc , f eatc ])
Changing of the position and features of the saddle Sright stored in the S-Table and the catastrophe Ccat stored in the C-Table. If the ordering of catastrophe points changes, update the MSSS
accordingly. Illegal relations, if exist, must be properly updated.
cost(SC(Sright → [poss , f eats ], Ccat → [posc , f eatc ])) =
wsc | Sright − [poss , f eats ] | +wc | Ccat − [posc , f eatc ] |
2. Left-Relabeling L(Slef t → Snew )
Changing of the leftport from Slef t to Snew which corresponds to changing of the nesting relations from Sright being nested inside Slef t to Snew .
cost = wl | Sright Slef t − Sright Snew |
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12
Scale
Saddles-Based MSSSs
Scale
5.2
3.0
3.0
C1
2.0
2.0
1.0
0.0
0.0
C1
1.0
1.0
E1
2.0
3.0
S0
Space
4.0
E0
0.0
0.0
1.0
E0
2.0
S0
(a)
3.0
4.0
E1
Space
(b)
Figure 5: The deep structures of SA and SB
3. Deletion D(Slef t Ccat Sright → φ)
Deleting a node Slef t Ccat Sright from the MSSS. Illegal relations, if exist, must be properly
updated.
cost(D(Elef t Eright Ccat )) = wd stability(Ccat )
5.2.2
Simple Example
We consider the image matching of the two images as described in Section 5.1.2 but using SaddleBased MSSSs instead. The deep structures of the Saddle-Based MSSSs SA and SB are shown in
Figure 5(a) and Figure 5(b), respectively.
Let SA be a Saddle-Based MSSS consisting of one node: [C1 S1 ] with
TS =
TC =
S1 : pos = 3
,
C1 : pos = 2, scale = 2
,
and SB be another Saddle-Based MSSS consisting of one node: [C1 S1 ] with
TS =
TC =
S1 : pos = 2
,
C1 : pos = 3, scale = 3
.
The series of the edit operation that transform SA to SB through a common MSSS SC is
SC(C1 → [pos = 3, scale = 3], S1 → [pos = 2])
The series of delete operation is empty and the common MSSS SC equals SB . The edit cost edit(SA →
SB ) is then
edit(SA → SB ) = cost(SC(C1 → [pos = 3, scale = 3], S1 → [pos = 2]))
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5.3
Discussion
The only operation needed to edit SA to SB is to corrects the position and scale of the catastrophe C1
and the position of the saddle S1 . The SA after applying the edit operations is [C1 S1 ] with
TS =
TC =
S1 : pos = 2
,
C1 : pos = 3, scale = 3
.
which is now equivalent to SB .
5.3
Discussion
We propose to calculate the edit distance as a linear combination of the transition cost, where the
coefficients of the linear combination is estimated as a best fit with hand classified images. This
estimation has not yet been performed since the edit distance algorithm has not yet been completed.
We suggest that the coefficients are all first set to one and then adjusted according to the discrepancy between the matching results and the hand classified images until the best fit is obtained.
6
Medical Image Database
This Section describes an image database of the craniofacial area. The database consists of four threedimensional volumetric CT images obtained from four patients with various abnormalities and 113
masks of the teeth segmented manually. The database is aimed at assessing the performance of the
image matching algorithm based on the Multi-Scale Singularity Trees (MSSTs). The performance of
the matching algorithm can be measured by the rate of correct matching between teeth of the same
kind at different poses and locations.
6.1
CT Images and Tooth Masks
The image database consists of four three-dimensional volumetric CT images obtained from four
patients with various abnormalities and 113 masks of the teeth segmented manually. The teeth are
segmented using the ∇V ision[2], the multi-scale interactive segmentation tool, installed at the 3DLab. ∇V ision is a joint development between researchers from the IT University of Copenhagen and
the University of Copenhagen. The program has been develop under the project “Computing Natural
Shape" and as a project in the now closed company Generic Vision.
All images are stored in the ANALYZETM 7.5 file format[1] with big-endian encoding. According
to the ANALYZETM 7.5 file format, two separate files with the same name but different endings are
used for an image, the header ended with .hdr and the raw data ended with .img. The CT images
are stored in different directories namely p3, p4, p5, and p6 together with the segmented tooth
masks named with number. The ∇V ision project files ended with .prj can also be found in the
corresponding directories.
The volumetric craniofacial CT images use a 32-bit real number for a voxel. The segmented tooth
masks use an 8-bit integer for a voxel, the value zero for the background and the value 254 for the
object. The properties of the images and the number of the masks of segmented teeth for each CT
image are listed in Table 1. Figure 6. shows the surfaces of the segmented teeth of the four CT images.
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6.1
CT Images and Tooth Masks
Name
P3
p4
p5
p6
Dimension
Size(mm)
399 × 232 × 107 0.37 × 0.37 × 0.80
302 × 196 × 84 0.49 × 0.49 × 1.00
305 × 199 × 160 0.49 × 0.49 × 0.60
288 × 238 × 73 0.47 × 0.47 × 1.00
Total Number of Segmented Teeth
Number of Segmented Teeth
44
32
8
29
113
Table 1: Properties of the CT images in the database
p4
p3
p6
p5
Figure 6: Surfaces of the segmented teeth of the four craniofacial CT images
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6.2
Computational Task
6.2
Computational Task
The database is aimed at assessing the performance of the image matching algorithm based on the
Multi-Scale Singularity Trees (MSSTs)[15, 16]. The medical task interested is the detection and location of teeth in CT/MR images, which is useful for a number of interesting problems such as studying
the statistics of tooth shapes, studying the growth pattern, and analyzing the configuration of teeth for
possible corrections.
For each segmented tooth, an MSST is computed. The similarity between two segmented teeth is
measured as the total cost of edit operations that transform one MSST to the other. This problem is
commonly known as the Tree Edit Distance (TED) problem[4].
In order to find the best match, the enquiry tooth have to be matched against all other teeth in the
database. Fortunately, the MSSTs of all the segmented teeth can be pre-computed and later reused in
the matching process. As a sub-goal, if the matching algorithm permits, MSSTs of teeth will be used
to match into the MSSTs computed from the craniofacial CT images of a larger area to evaluate the
possibility of sub-object extractions.
6.3
Assessment
The set of plastic teeth prepared by human experts can be used as the ground truth. The performance
of the matching algorithm can be measured by the rate of the correct matching between teeth of the
same kind at different poses and locations. The matching results will include the best match and all
other matches ranked according to the distance or similarity.
7
Concluding Remarks
The selection of landmarks and the energy functional define the tessellation of energy partitions. The
nesting of energy partitions in scale-space creates hierarchies which are captured and presented using
tree structure called Multi-Scale Singularity Trees (MSSTs). MSSTs capture the deep structure of
images. MSST transitions observed experimentally from a series of generated images are categorized
into three categories: changes of positions, changes of relations, and changes of connections. We have
not yet clearly proved the completeness of the obtained transitions.
MSSTs and introduced MSSSs are equivalent. Both structures topologically represent the deep
structure of images. It’s however easier and more intuitive to study and define edit operations on
MSSSs since their transitions are simpler[16]. We present the two set of edit operations together
with their cost estimates each on Extrema-Based MSSSs and Saddles-Based MSSSs. Since the edit
operations on Saddles-Based MSSSs are simpler, we prefer the Saddles-Based MSSSs as a multi-scale
representation of images suitable for image matching applications.
Creation events and annihilation events between pairs of critical points that cannot be tracked down
to the first-scale image are ignored. These catastrophic events, however generic, are not frequently
occurred. Including these catastrophic events in MSSTs/MSSSs and defining edit operations that can
handle these events might improve the matching results.
MSSSs are preferable for image matching applications however one of the advantages of MSSTs
over MSSSs is the hierarchical grouping of energy partitions suggested by its sub trees. We believe
that exploiting this advantage might be useful for sub-object extraction, e.g. matching MSSS of a
sub-object with MSSS equivalent to a sub tree cut from the MSST of the whole image.
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