Bioinspired Polarization Imaging Sensors

Bioinspired Polarization Imaging Sensors
Bioinspired Polarization
Imaging Sensors: From
Circuits and Optics to Signal
Processing Algorithms and
Biomedical Applications
Analysis at the focal plane emulates nature’s method in sensors to image and
diagnose with polarized light.
By Timothy York, Member IEEE , Samuel B. Powell, Shengkui Gao, Lindsey Kahan,
Tauseef Charanya, Debajit Saha, Nicholas W. Roberts, Thomas W. Cronin,
Justin Marshall, Samuel Achilefu, Spencer P. Lake,
Baranidharan Raman, and Viktor Gruev
In this paper, we present recent work on
aspects of these sensors. First, we describe the electro–optical
bioinspired polarization imaging sensors and their applications
challenges in realizing a bioinspired polarization imager, and in
in biomedicine. In particular, we focus on three different
particular, we provide a detailed description of a recent lowpower complementary metal–oxide–semiconductor (CMOS)
polarization imager. Second, we focus on signal processing
Manuscript received May 31, 2014; accepted July 17, 2014. Date of publication
August 20, 2014; date of current version September 16, 2014. This work was
supported in part by the U.S. Air Force Office of Scientific Research under
Grants FA9550-10-1-0121 and FA9550-12-1-0321, the National Science Foundation
under Grant OCE 1130793, the National Institutes of Health under Grant
1R01CA171651-01A1, and a McDonnell Center for System Neuroscience grant.
T. York, S. B. Powell, S. Gao, and V. Gruev are with the Department of Computer
Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130 USA
(e-mail: [email protected]; [email protected]; [email protected];
[email protected]).
L. Kahan and S. P. Lake are with the Department of Mechanical Engineering and
Materials Science, Washington University, St. Louis, MO 63130 USA (e-mail:
[email protected]; [email protected]).
T. Charanya and S. Achilefu are with the Department of Radiology, Washington
University School of Medicine, St. Louis, MO 63110 USA (e-mail: [email protected];
[email protected]).
D. Saha and B. Raman are with the Department of Biomedical Engineering,
Washington University, St. Louis, MO 63130 USA (e-mail: [email protected];
[email protected]).
N. W. Roberts is with the School of Biological Sciences, University of Bristol, Bristol
BS8 1UG, U.K. (e-mail: [email protected]).
T. W. Cronin is with the Department of Biological Sciences, University of Maryland
Baltimore County, Baltimore, MD 21250 USA (e-mail: [email protected]).
J. Marshall is with the Sensory Neurobiology Group, University of Queensland,
Brisbane, Qld. 4072, Australia (e-mail: [email protected]).
This paper has supplementary downloadable material available at http://, provided by the authors. The material presents videos of Figs. 7, 8,
and 19–20. Contact Viktor Gruev at [email protected] for questions regarding the
multimedia material.
Digital Object Identifier: 10.1109/JPROC.2014.2342537
algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation.
Third, the emergence of these sensors has enabled rapid
progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical
areas, such as label-free optical neural recording, dynamic
tissue strength analysis, and early diagnosis of flat cancerous
lesions in a murine colorectal tumor model. We highlight
results obtained from these three areas and discuss future
applications for these sensors.
| Bioinspired circuits; calibration; complementary
metal–oxide–semiconductor (CMOS) image sensor; currentmode imaging; interpolation; neural recording; optical neural
recording; polarization
Nature provides many ingenious ways of sensing the surrounding environment. Sensing the presence of a predator
might mean the difference between life and death.
0018-9219 Ó 2014 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/
redistribution requires IEEE permission. See for more information.
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Detecting the presence of food means the difference between starvation and survival. Catching a signal from afar
could result in finding a mate. For these and numerous
other scenarios, organisms have evolved many different
structures and techniques suitable for their own survival.
Mimicking nature’s techniques with modern technology
has the potential for engineering unique sensors that can
enhance our understanding of the world.
Of the senses evolved by nature, vision provides some
of the most varied examples to emulate. From the compound eyes of invertebrates to the human visual system,
with many other subtle variations found in nature, vision is
a very powerful way for organisms to interact with the
environment. Vision is such an important sense that
image-forming eyes have evolved independently over 50
times [1].
The purpose of all eyes is to convert light into some sort
of neural signaling interpreted by the brain. Photosensitive
cells within the eye act as photoreceptors, triggering a
chain of action potentials when they sense light. In some
animals, these photosensitive cells detect different wavelengths of light through pigmented cells, resulting in color
vision. In other animals, integration of microvilli above the
photosensitive cells has allowed polarization-sensitive
A variety of electronic sensors have been developed
to mimic biological vision. These sensors have found
wide use across many different fields. From astrophysics
to biology and medicine, electronic image sensors have
revolutionized the scientific understanding of the world.
Similar to animal vision, these electronic sensors also
contain a photosensitive element, called a pixel, that
produces a change in voltage or current when light
converts into electron–hole pairs. Sampling this output
at given integration times results in a signal proportional
to the intensity of light during this integration period.
Color selectivity can also be implemented by matching
spectral filters directly to the pixels, similar to pigmentation in animals [2]. Most color image sensors are constructed by monolithically integrated pixel-pitch-matched
color filters (e.g., red, green, and blue color filters) with
an array of complementary metal–oxide–semiconductor
(CMOS) or charge-coupled device (CCD) detectors, producing color images in the visible (400–700 nm) spectrum [3], [4].
Some modern image sensors can detect polarization
information present in light [5]–[13]. Advances in nanofabrication technology have allowed for the integration of
polarization filters directly onto photosensitive pixels, in a
similar fashion to color sensors [14]–[18]. These polarization sensors contain no moving parts, operate at real-time
or faster frame rates, and can use standard lenses. This new
type of polarization sensor has opened up new avenues of
exploration of polarization phenomena [19]–[21].
In this paper, we present recent work on bioinspired
polarization imaging sensors and its applications to
biomedicine. We begin with a brief theoretical discussion
of the polarization properties of light that provides the
framework for realizing bioinspired polarization sensors in
CMOS technology. Next, we give a discussion of some of
the devices which are used for polarization detection,
including many bioinspired polarization sensors. We include the design of a current mode, CMOS polarization
sensor we have developed. We discuss the many signal
processing challenges this new class of polarization sensors
require, from calibration and interpolation, to human
interpretable display. Next, we include a systematic optical
and electronic method of testing these new types of sensors. We finally conclude with three biomedical applications of these sensors. We use the bioinspired current
mode sensor to make in vivo measurements of neural
activity in an insect brain. We further demonstrate that a
bioinspired sensor can measure the real-time dynamics of
soft tissue. We finally show how a bioinspired polarization
sensor can be used as a tool to enhance endoscopy.
Polarization is a fundamental property of electromagnetic
waves. It describes the phase difference between the x
and y components of the electromagnetic field when it is
viewed as a propagating plane wave
E ¼ E0;x cosð!t kz þ x Þ^x þ E0;y cosð!t kz þ y Þ^y (1)
where E0;x and E0;y are the respective amplitudes of the x
and y fields, ! is the frequency, t is the time, k is the wave
number, z is the direction of propagation, and x and y are
the respective phases.
From (1), the type of polarization is characterized from
x y , because the difference in the phases is what shapes
the wavefront of the propagating waves. If the phase difference is random, the light is unpolarized; if the phases
are the same, the light is linearly polarized; if the phases
are unequal but constant, the light is elliptically polarized.
A special case of elliptical polarization is observed when
the phase difference is exactly =2, which transforms the
wavefront into a circle, and so the propagating light is
termed circularly polarized. Most of the light waves encountered in nature are partially polarized, a linear combination of unpolarized light waves and completely polarized
light waves.
A. Mathematical Treatment of Light Properties via
Stokes–Mueller Formalism
The classic treatment of incoherent polarized light uses
the Stokes–Mueller formalism [22]. The mathematical
framework for polarized light is derived mostly from the
seminal work by Sir Gabriel Stokes. From his work, the
intensity of light measured through a linear polarizer at
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York et al.: Bioinspired Polarization Imaging Sensors
angle and a phase retarder can be mathematically
represented as
Similarly, the DoCP (4c) measures how circularly polarized the light is, with 0 being no circular polarization and 1
being completely circularly polarized
Ið; Þ ¼ ðS0 þ S1 cos 2 þ S2 sin 2 cos 2
þ S3 sin 2 sin Þ: (2)
The terms S0 through S3 are called the Stokes parameters,
and each describes a polarization property of the light
wave. The S0 parameter describes the total intensity; S1
describes how much of the light is polarized in the vertical
or horizontal direction; S2 describes how much of the light
is polarized at 45 to the x=y-axis along the direction of
propagation; and S3 describes the circular polarization
properties of the light wave. The Stokes parameters are
commonly expressed as a vector, which relates these parameters to the electromagnetic wave equation (1)
The second metric is the angle of polarization (AoP),
which gives the orientation of the polarization wavefront.
This is the angle of the plane that the light wave describes
as it propagates in space and time and is computed as
AoP ¼ tan1
E20;x þ E20;y
6 S1 7 6 E2 E2 7
0;y 7
6 7 ¼ 6 0;x
4 S2 5 4 2E0;x E0;y cos 5:
2E0;x E0;y sin (5)
In (3), is the phase difference between the two orthogonal components of the light wave x y .
Treating the Stokes parameters as a vector allows for
the easy superposition of many incident incoherent beams
of light, which allows for an elegant mathematical treatment of light properties, ranging from unpolarized to partially polarized and completely polarized light. This is
achieved by expressing the light as the weighted summation of a fully polarized signal and a completely unpolarized signal. Furthermore, Mueller matrices can be used
to model the change in polarization from interaction with
optical elements (reflection, refraction, or scattering) during light propagation in a medium, such as lenses, filters,
or biological tissue [23]. A Mueller matrix is a 4 4 realvalued matrix that mathematically represents how an optical element changes the polarization of light. The change
in polarization is computed from the matrix–vector product of an incident Stokes vector S, with the matrix for a
component M.
Two additional parameters are typically computed
from the four-element Stokes vector. The first parameter is
the degree of polarization (DoP), which estimates how
much of the light is polarized. The DoP is computed from
(4a) and is measured on a scale from 0 to 1, with 0 being
completely unpolarized and 1 being completely polarized.
The DoP can be further expressed in two components, the
degree of linear polarization (DoLP) and the degree of
circular polarization (DoCP). The DoLP (4b) measures
how linearly polarized the light is, with 0 being no linear
polarization and 1 being completely linearly polarized.
S21 þ S22 þ S23
DoP ¼
S1 þ S22
DoLP ¼
DoCP ¼ :
B. Polarization of Light Through Reflection
and Refraction
Because polarization is a fundamental property of light,
many organisms have evolved the capability to detect it in
the natural world. To understand how this capability is
useful, it helps to understand how light becomes polarized.
In nature, light becomes polarized usually through reflectance or refractance of light off of an object, or through
scattering as it encounters particles as it propagates
through space. The DoP of the emerging light wave, after
interacting with a surface, is based on the relative index of
refraction between the reflecting material and medium of
propagation, as well as the angle of reflection. The Mueller
matrix for light reflection from a surface is
Mreflect ¼
1 tan 2 sin þ
cos2 þ cos2 þ
B cos2 cos2 þ
cos2 cos2 þ
cos2 þ cos2 þ
2 cos þ cos 0
2 cos þ cos (6)
where is the incident angle i subtracted from the
refracted angle r , and þ is the addition of i and r . The
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
following equation presents the Mueller matrix of the light
refracted through the surface:
Mrefract ¼
sin 2i sin 2r
2ðsin þ cos Þ2
cos2 þ 1 cos2 1
B cos2 1 cos2 þ 1
2 cos 0
2 cos 1
The incident and refracted angles are related by Snell’s
law, which relates the index of medium 1 ðn1 Þ and the
incident angle ði Þ to the index of medium 2 ðn2 Þ and the
refracted angle ðr Þ
n1 sin i ¼ n2 sin r :
The Stokes vector for light reflected from a surface can be
computed by multiplying the incident Stokes vector with
the Mueller matrix of reflection from the surface (6). Assuming an incident unpolarized light (i.e., Sin ¼
½1 0 0 0T ), computing the reflected light Sout ¼ S Mreflect , for all possible incident angles (0 to 90 ),
results in a graph like Fig. 1, which is an example using air
ðn1 ¼ 1Þ and glass ðn2 ¼ 1:5Þ as the two indices of refraction.
In Fig. 1, the black line represents reflection and the gray
line represents refraction of light. As can be seen in Fig. 1,
Fig. 1. Degree of linearly polarized light for both reflected and
refracted light as a function of incident angle. In this example,
air ðn1 ¼ 1Þ and glass ðn2 ¼ 1:5Þ are the two indices of refraction.
The maximum degree of linear polarization occurs at the Brewster
angle, information that can be used to identify the index of
refraction of a material.
the DoLP for glass has a maximum value of 1 for an incident
angle of 56.7 . This angle is known as the Brewster angle,
and it is often used to determine the index of refraction of a
material in instruments such as ellipsometers.
This same concept has been utilized in nature. For
example, water beetles, which are attracted to the horizontally polarized light that reflects off of the surface of
water, typically land on the water surface at 53 , which is
the Brewster angle of water [24]. By measuring the maximum polarization signatures of the reflected light as a
function of incident/reflected angle, water beetles estimate the Brewster angle of the water surface and possibly
uniquely determine the location of water surfaces.
C. Polarization of Light Through Scattering
Light scatters when it encounters a charge or particle in
free space. The charge or particle impacts the electric field
as the field propagates through space, and this influence
can affect the polarization state of the light. An example is
the Rayleigh model of the sky. In the Rayleigh model, light
scattered from a particle in a direction orthogonal to the
axis of propagation becomes linearly polarized. This ballistic scattering from the many particles in the atmosphere
creates a polarization pattern in both DoLP and AoP across
the sky based on the position of the sun. In nature, the
desert ant Cataglyphis fortis uses this polarization pattern of
the sky to aid its navigation to and from home [25].
Honeybees also use sky polarization as part of their ‘‘waggle
dance’’ to indicate the direction of food [26]. There is even
increasing evidence that birds combine magnetic fields and
celestial polarization for navigation purposes [27], [28].
Optical scattering is present in biological tissue as well.
The scattering agents for light as it propagates through
tissue include cells, organelles, and particles, among
others. Because many of these components can be on the
order of the wavelength of the propagating light, the Mie
approximate solution to the Maxwell equations, which is
typically referred to as the Mie scattering model, can be
used to describe the effects of scattering on polarization.
Absorption by tissue attenuates the intensity of light, while
scattering causes a depolarization of light in the general
direction of propagation. The density of the scattering
agents in a tissue influences the depolarization signature of
the imaged tissue. For example, high-scattering agents are
typically found in cancerous tissue, which leads to depolarization of the reflected or refracted light from a tissue.
Hence, there is a high correlation of light depolarization with
cancerous and precancerous tissue, and detecting polarization of light can aid in early detection of these tissues [20].
Natural biological designs have served as the motivation for many unique sensor topologies. Real-time (i.e.,
30 frames/s), full-frame image sensors [4] are a simple
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York et al.: Bioinspired Polarization Imaging Sensors
approach to a visual system, capturing all visual information at a given time. However, this typically creates bottlenecks in data transmission, as well as non-real-time
information processing due to the large volume of image
data presented to a digital processor such as computer,
digital signal processors (DSPs), or field-programmable
gate arrays (FPGAs). Signal processing at the focal plane,
as it is typically performed in nature, can lead to significant
reduction of data that are both transmitted and processed
off-chip. Hence, sparse signal processing, as is found in the
early visual processing in many species, such as the mantis
shrimp, can serve as inspiration for efficient, low-power
artificial imaging systems [29].
In the mid-1980s, a new sensor design philosophy
emerged, where engineers looked at biology to gain understanding in developing lower power visual, auditory, and
olfactory sensors. Some early designs [30] attempted a
complete silicon model of the retina, using logarithmic
photoreceptors with resistive interconnects to produce an
array whose voltage at a location is a weighted spatial average of neighboring photoreceptors. Other designs sought to
replicate neural firing patterns by asynchronously outputting only when detecting significant changes from
each photosensitive pixel [31]–[33] or significant color
changes from color-sensitive pixels [34], [35]. Some designs have even sought to directly mimic the compound
eye of insects [36].
One of the main benefits of these systems has been a
low-power and real-time realization of information extraction at the sensor level. These sensors have found a niche
in various remote-sensing applications where power is a
major constraint for sensor development [37]. Furthermore, in these applications, extracting information at the
sensor level and transmitting preprocessed data can greatly
reduce bandwidth and overall power consumption.
A. Overview of Classical Polarization
Imaging Sensors
The polarization selectivity depends on the ability to
measure the Stokes parameters. From (2), the intensity of
light measured with a linear polarizer with a retarder depends on the angle of the linear polarizer ðÞ, the phase
retardance ðÞ, and the four Stokes parameters. A unique
solution for the Stokes parameters in (2) thus requires a
number of measurements equal to the number of desired
Stokes parameters.
To determine all four Stokes parameters, four distinct
measurements are made with linear polarization filters
and quarter-wave retarders. Hence, the four Stokes parameters can be determined as follows:
S0 ¼ Ið0 ; 0 Þ þ Ið90 ; 0 Þ
6 S1 ¼ Ið0 ; 0 Þ Ið90 ; 0 Þ 7
4 S2 ¼ Ið45 ; 0 Þ Ið135 ; 0 Þ 5:
S3 ¼ S0 2Ið45 ; 90 Þ
In these equations, Ið0 ; 0 Þ is the intensity of the e-vector
filtered with a 0 linear polarization filter and no phase
retardation, Ið45 ; 0 Þ is the intensity of the e-vector
filtered with a 45 linear polarization filter and no phase
retardation, and so on. The fourth Stokes parameter is
computed with a 45 linear polarization filter and a
quarter-wave retarder.
The most predominant method of Stokes measurement
solves these equations by rotating a linear polarization filter
and retarder in front of the sensor, capturing a static image at
each rotation. This type of sensor is called a division-of-time
polarimeter [38], since it requires capturing the same scene
at multiple steps in time. This simple design suffers from a
reduced frame rate, as each complete set of measurements
requires multiple frames. It also requires a static scene for the
duration of the measurement, since any change in the scene
between rotations would induce a motion blur. As this is the
simplest method for measuring static scenes, division-of-time
polarimeters have realized a number of applications, from
3-D shape reconstruction [39], haze reduction [40], mapping
the connectome [41], and many others.
An alternate method with static optics projects the
same scene to multiple sensors. Each sensor uses a different polarizer and/or retarder in front of the optical sensor
to solve for the different Stokes parameters. This type of
modality is called division of amplitude [38] since the same
optical scene is projected full frame multiple times at
reduced amplitude per projection. The drawback to this
system can be the bulk and expense of having a large array
of optics and multiple sensors. Maintaining a fixed alignment of the optics so all sensors see the same coregistered
image also poses a challenge to this polarization architecture, which typically requires image registration in software. These types of instruments have found some use in
unmanned aerial vehicle (UAV) applications [42], [43],
target detection in cluttered environments [44], and
measuring the ocean radiance distribution [45]. A similar
optically static method uses optics to project the same
scene to different subsections of a single sensor. Each
subsection contains a different analyzer to solve for the
Stokes parameters. This type of sensor is called a divisionof-aperture polarimeter [38], since the aperture of the
sensor is subdivided for polarization measurement of the
same scene. The advantage is that it requires only one
sensor, but the disadvantage is that it is prone to
misalignment and can contain a long optics train. Multiple
scene sampling on the same array also reduces the effective
resolution of the sensor, without the possibility of
upsampling through interpolation. The system complexity,
from maintaining the optical alignment to the image
processing, has precluded them from wider use.
B. Bioinspired Polarization Imaging Sensors
Taking a cue from nature, however, would mate the
polarization analyzers directly to the photosensitive element. Fig. 2 (left) shows an example of how nature has
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Fig. 2. (Left) The compound eye of the mantis shrimp, where
ommatidia combine polarization-filtering microvilli with
light-sensitive receptors. (Right) A bioinspired CMOS imager
constructed with polarization sensitivity, where aluminum
nanowires placed directly on top of photodiodes act as linear
polarization filters.
evolved polarization-sensitive vision. The compound eye of
the mantis shrimp contains a group of individual photocells called an ommatidium. Each ommatidium has a
cornea that focuses external light. The focused light is
filtered through a pigment cell for color sensitivity and
passes through a series of photosensitive retinular cells
(R-cells). In the mantis shrimp, these cells contain an
array of microvilli that can act as polarization filters. The
photosensitive R-cells will signal the brain via the optic
nerve, and the brain extracts visual information based on
input from the array of ommatidia.
Biomimetic approaches have also attempted to replicate the polarization sensitivity present in certain species.
Early designs integrated liquid crystals [46] or birefringent
crystals [5] directly to pixels. These sensors allowed for
full-frame polarization contrast imaging. More advanced
polarization sensors integrated filters at multiple orientations, which enabled capture of the first three Stokes parameters [12], [47], [48].
Further advances in nanotechnology and monolithic
integration of nanowires with CMOS technology have
enabled high-resolution versions of this paradigm [6]. The
use of liquid crystal polymers and dichroic dyes has allowed a full Stokes polarimeter [10], [49]. These sensors
are capable of capturing polarization information at video
frame rates, and their compact realization has allowed
them to pertain to remote-sensing applications, such as
underwater imaging [21].
Other polarization sensor designs have attempted alternate, more biologically pertinent designs. As octopuses
are known to have polarization-sensitive vision [50], a design based on polarization contrast with a resistive network sought to replicate the octopus vision system in
silicon [9]. Polarization sensor designs have been developed that utilize asynchronous address event mode to
output only when there are large enough changes in polarization contrast [51]. In this work, both the ommatidia
functionality and neural processing circuitry have been
efficiently implemented in CMOS technology.
Analogous to the microvilli in the mantis shrimp vision
system [52], which function as polarization-filtering elements, bioinspired polarization sensors use pixel-matched
aluminum nanowire polarization filters at 0 , 45 , 90 ,
and 135 , arrayed in a 2-by-2 grid called a superpixel [6],
[14] (see Fig. 2, right). These filters are fabricated to be
70 nm wide and 200 nm tall and have a horizontal pitch of
140 nm. The filters are deposited postfabrication of the
CMOS imager through an interference lithography process, matching the pixel pitch of the imager array of
7.4 m by 7.4 m. Maintaining an air gap between these
filters allows for a higher extinction ratio than does embedding the filters within a layer of silicon dioxide [16].
With the maturity of nanofabrication techniques,
many interesting optical designs have become feasible.
For example, metamaterial surfaces acting as achromatic
quarter-wave plates [18] or as high-extinction ratio
polarization filters [15] can further advance the field of
polarization imaging when integrated with an array of
imaging elements. These advances will bring the complete
imaging system design closer to biology in terms of
sensitivity and selectivity to both spectral and polarization
information. Foundries such as TowerJazz Semiconductor
(Migdal Haemek, Israel), Dongbu HiTek (Bucheon,
Korea), LFoundry (Avezzano, Italy), and TSMC (Hsinchu,
Taiwan) already offer specialized CMOS fabrication
processes explicitly optimized for image sensors. However, polarization-filtering capabilities are not included in
regular image sensor fabrication. The key would be to
integrate these emerging optical fabrication techniques
with these specialized CMOS fabrication technologies at
the foundry level for optimal optical performance and
high yield. With such an integrated solution, future
polarization imaging designs could incorporate low-power
analog circuitry that mimics neural circuitry, leading to
sparse on-chip computation.
C. Bioinspired Current-Mode Imaging Sensor With
Polarization Sensitivity
We have designed a bioinspired polarization imaging
sensor by combining CMOS imaging technology with nanofabrication techniques to realize linear polarization filters.
In this bioinspired vision system, the photosensitive
elements are monolithically integrated with aluminum
nanowires, or microvilli, acting as linear polarization filters.
The bioinspired photosensitive element is based on a currentmode CMOS imaging paradigm. The signal from the diode is
linearly converted into a current inside the pixel, and the
image is then formed from each of the independent pixels.
Circuitry on the pixel and for readout is presented in
Fig. 3. The pixel consists of a charge transfer transistor
(M1), reset transistor (M2), transconductance amplifier
(M3), and select transistor (M4). Through a series of
switching multiplexers, the output of the pixel connects
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York et al.: Bioinspired Polarization Imaging Sensors
Fig. 3. Current-mode pixel schematic and peripheral readout circuitry of the imaging sensor. The pixel’s readout transistor operates in the
linear mode, allowing for high linearity between incident photons on the photodiode and output current from the pixel.
either to a reset voltage Vreset or to the readout current
conveyor. This bus-sharing methodology eliminates the
need for two separate buses to separately connect the drain
of the readout transistor and the output current bus, which
reduces the pixel pitch. The transconductance amplifier
(M3), also known as the readout transistor, is biased to
operate in the linear mode. This ensures a linear relationship between an output drain current and input photovoltage applied at the gate of transistor M3. The linearity is
critical in correcting threshold offset mismatches between
readout transistors via a technique known as correlated
double sampling (CDS).
Current-mode image sensors rely on current conveyors
to copy currents from the pixels to the periphery while
providing a fixed reference voltage to the input node, that
is, to the drain node of the pixel’s readout transistor (M3).
The classic current conveyor design [53] uses four transistors, two n-channel (NMOS) and two p-channel (PMOS)
metal-oxide-semiconductor transistors, in a complementary configuration. The design is compact, but the output
impedance is limited, and the transistors are subject to
nonlinearity due to channel length modulation. Furthermore, the voltage at the input terminal of the current
conveyor (i.e., the voltage at the drain node of the pixel’s
readout transistor) can vary as much as 20% for the typical
input current from a pixel. A single transistor design [54]
improves settling time and power consumption but decreases linearity of the output current.
Because the polarization information conveyed in the
S1 and S2 parameters is based on the linear difference in
pixel intensities, pixel linearity is crucial to accurate polarization measurement. Alternate current conveyor designs use an operational amplifier with a transistor in the
feedback path. The conveyor has high linearity and can be
used for novel current-mode designs [55], but at the cost of
increased power consumption and area.
To improve the performance of the output current
conveyor, a regulated cascoded structure for the current
conveyor is used. Since all transistors in the current conveyor
operate in the saturation mode, the potentials on the gates of
transistors M13 and M14 are set by a biasing current. Since
the gate potential of M13 (M14) and drain potential of M15
(M16) are the same, the channel length modulation effect is
eliminated between the two branches, and the two drain
currents are the same. Furthermore, the impedance of the
output branch is increased due to the regulated cascode
structure by a factor of ðgm ro Þ2 , where gm is the
transconductance and ro is the small signal output impedance.
The high output impedance of the output branch is important
when supplying a current to the next processing stage. This
improved performance does come at a cost of increase in chip
area compared with the aforementioned implementations.
The row-parallel current conveyors set the reference
voltage on the output bus and copy the current from the
pixel to the output branch, using transistor M20 to switch
along the pixels in the column. The current conveyors are
implemented by connecting two current mirrors in a
negative feedback configuration. Transistors M11–M16
form a PMOS-regulated cascode current mirror connected
with an NMOS-regulated cascode current mirror composed of transistors M5–M8. Transistors M13 and M14
operate in the saturation region, and the gate-to-source
potentials are set by a reference current source of 1 A.
Hence, the drain nodes of transistors M15 and M16 are at
the same potential. Transistors M11 and M12 provide negative feedback to transistors M13 and M14, respectively,
ensuring that all transistors remain in the saturation mode
of operation. Since transistors M15 and M16 have the same
source, gate, and drain potential, the drain currents flowing through these two transistors are the same.
Transistors M7 and M8 pin the drain voltage of transistors M5 and M6 because the bias current through these
transistors sets the gate voltage on each, respectively.
Since the currents are the same flowing through transistors
M5 and M6, and since the gate and drain potentials are the
same for these transistors, the drain potential is the same
for these transistors. Therefore, the drain potential on
transistor M5 is set to Vref .
The readout transistor in the pixel (M3) is designed to
operate in the linear current mode by ensuring that the
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
drain potential of the M3 transistor is lower than the gate
potential by a threshold during the entire mode of operation. This is achieved by setting the Vref bias potential to
0.2 V and resetting the pixel, which sets the gate voltage of
M3 to 2.7 V. Since the threshold voltage of the transistor is
0.6 V, the lower limit on the gate of M3 transistor is set
to 0.7 V in order to operate in the linear mode. The output
current from transistor M3 is described by
Iphoto ¼ n Cox
ðVphoto VTH;M1 ÞVref ref : (10)
In (10), n is the mobility of electrons, Cox is the gate
capacitance, and Vth is the threshold voltage of the transistor. The current conveyor holds Vref on the drain of the
readout transistor M3. By keeping Vref constant, the output
current is linear with respect to the photovoltage.
The pixel timing is shown in Fig. 4. During FD Reset,
reset transistor M2 and select transistor M4 are activated.
With these transistors activated, setting the voltage on the
Out node of the pixel to Vreset drives the floating diffusion
node Vfd to the reset potential. After resetting the floating
diffusion, the reset value can be read out during Reset
Readout for difference double sampling. During the Pixel to
FD stage, the charge transfer transistor M1 activates, placing the integrated photovoltage onto the floating diffusion.
After turning M1 off, readout of all the pixels in the row
takes place. M1 reactivates during Pixel Reset, after which
the Out switches back to Vreset , and M2 reactivates, pulling
the photodiode up to the reset potential. All three switch
transistors turn off, and the readout proceeds to the next
The pixel’s layout is implemented in a 180-nm-feature
CMOS image sensor process with pinned photodiode capabilities. Fig. 5 shows a schematic of the pixel. The charge
transfer transistor is highly optimized to allow full transfer
of all charges from the photodiode capacitance to the
Fig. 4. Timing diagram for operating a current-mode pixel. The
timing information is provided from digital circuitry placed in the
periphery of the imaging array.
Fig. 5. Cross section of the pinned photodiode together with the reset,
transfer, readout, and select transistors. The diode is an n-type diode
on a p-substrate with an insulating barrier between. The readout
transistor operates as a transconductor, providing a linear
relationship between accumulated photo charges and an
output current.
floating diffusion, with the node heavily shielded for light
sensitivity. This node is capable of holding electron
charges with no significant losses for over 5 ms at an
intensity of 60 W/cm2 .
The recent introduction of bioinspired polarization image
sensors has opened up several research areas in signal
processing dealing with how best to reconstruct polarization images from measured data. In this section, we
highlight three such research areas: 1) calibration of
optical performance due to defects at the nanoscale;
2) spatial interpolation for increased polarization accuracy; and 3) processing to visually interpret polarization
A. Calibration of Bioinspired Polarization Sensors
Calibration of bioinspired polarization sensors aims to
correct imperfections and variations of the pixelated
polarization filters due to their nanofabrication. Variations
in the dimensions of aluminum nanowires cause the optical properties of the pixelated polarization filters (namely,
transmission and extinction ratios) to vary by as much
as 20% across an imaging array composed of 1000 by
1000 pixels [56]. Fig. 6 presents a scanning electron microscope (SEM) image of nanowire pixelated polarization
filters, where dimensional variations, as well as damage
such as cracks, can be clearly observed. Better nanofabrication instruments can partially mitigate these
problems, at considerable expense, but will not completely
eliminate them. Thus, we take a mathematical approach,
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York et al.: Bioinspired Polarization Imaging Sensors
A minimum of five measurements must be taken, but increasing n will reduce the impact of noise on the
Once the parameters are learned, the incident Stokes
vector can be reconstructed via
Sin Aþ ð~
However, if the intent is to use more sophisticated
reconstruction methods such as interpolation, then the
parameters can instead be used to transform the measurement into what an ideal superpixel would measure
Iideal Aideal Aþ ð~
dÞ ¼ G ð~
Fig. 6. SEM image of pixelated polarization filters fabricated via
interference lithography followed by reactive ion etching. Variations
between individual nanowires lead to variation of the optical response
of pixelated filters.
using mathematical models of the optics and imaging
electronics to compensate for nonidealities occurring at
the nanoscale [56].
Each pixel–filter pair’s response is modeled as a firstorder linear system according to
I ¼ ðg
0 0
0Þ M ~
Sin þ d ¼ ~
Sin þ d:
In this case, ~
I, A, and ~
d are the vertical concatenation of
each of the superpixel’s constituent pixels I, ~
A, and d,
The parameters A and ~
d can be learned for each
superpixel by measuring ~
I with n known values of ~
Sin and
performing a least squares fit as per
ðA ~
d Þ ¼ ð~
In Þ
This technique can correct for variations in the filters’
transmission and extinction ratios, orientation angles, and
even retardance as necessary. Reductions in reconstruction error from 20% to 0.5% have been achieved with this
mathematical model [56]. Fig. 7 shows the difference in
visual quality between uncalibrated and calibrated reconstructions of the DoLP. This calibration method not only
reduces the reconstruction errors but also eliminates the
fixed-pattern noise present from both filter nanofabrication and sensor integrated circuit fabrication.
The measured value I is the product of the top row of
the filter’s Mueller matrix M with the photodiode’s
conversion gain g and the Stokes vector of the incident
light ~
Sin , plus the photodiode’s dark offset d. In order to
correct for errors in the 4-D analysis vector ~
A, at least four
measurements must be considered simultaneously. The
typical case is to assume that ~
Sin is uniform across each
superpixel and thus treat each superpixel as a unit
I ¼ A~
Sin þ ~
B. Interpolation of Polarization Information
A second image processing challenge is interpolating
the correct polarization component from its neighbors, as
the DoFP array subsamples the image. Similar to the case
with color, many interpolation algorithms may be used,
from simple bilinear interpolation to more complex cubic
spline methods, each with varying degrees of accuracy
[57], [58]. An example is shown in Fig. 8. Because of the
pixelated filters, edges, such as the white spots on the dark
fish, can cause erroneous DoLP and AoP readings. Processing the image using bicubic interpolation greatly reduces these false polarization signatures.
The correlated nature of the polarization filter intensities does allow for some new interpolation methods specific to polarization. Some examples of these methods are
Fourier transform techniques [59], interpolation techniques based on local gradients [60], polarization correlations between neighboring pixels [61], and Gaussian
processes [62].
The performance of these different interpolation methods is usually evaluated both quantitatively and visually.
Mean square error (MSE) and the modulation transfer
function (MTF) [57], [58], [60] are regular quantitative
ways to measure the performance of an interpolation
method. MSE measures the difference in interpolated results compared with a known or generated ground truth
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Fig. 7. Uncalibrated (left) and calibrated (right) DoLP images of a moving van. Spatial variation in the optical response of individual
polarization pixels is removed using a matrix-type calibration scheme. This results in a more detailed and accurate DoLP (top) and AoP (bottom),
as can be seen by the emergence of the trees in the background. Refer to Video 1 in the supplementary material.
image. This allows evaluation of the optical artifacts
introduced during the interpolation step. The MTF, which
measures the spectrum of the point spread function, gives
Fig. 8. Importance of interpolation. Edge artifacts cause false
polarization signatures in both DoLP (inset, top right) and AoP
(inset, bottom right). Use of interpolation, in this instance bicubic,
significantly reduces these artifacts (inset, center column) and
results in greater accuracy. The data were taken with an underwater
imaging setup at Lizard Island Research Station in Australia. Refer to
Video 2 in the supplementary material.
an indication of the spatial fidelity of the sensor. When
used as an evaluator for interpolation techniques, it
demonstrates how well the given technique recovers
spatial frequencies beyond simple decimation. In spatially
bandlimited images, full recovery is possible using a fast
Fourier transform technique. In the more general case,
small, uniformly applied interpolation kernels, such as
bilinear- or bicubic-based interpolation, perform worse
than edge-detection-based [60] or local-prediction-based
[62] interpolation methods in terms of MSE. But they
have less computational complexity because they are
separable filters, which allows them to process images
with fewer mathematical operations for real-time display
(i.e., 30 frames/s at 1-megapixel spatial resolution).
The signal processing challenges for this new class of
bioinspired polarization imaging sensors are as important
as the actual imaging hardware (electronics and optics)
design. Polarization data generated from these sensors
without proper signal processing can lead to erroneous
conclusions, as can be seen in Fig. 8. In this example, high
polarization patterns across the fish are due to pixelation of
the polarization filters in the imager and are not observed
across the fish if the data are properly processed. Similar
artifacts can be observed when imaging cells and tissues, as
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York et al.: Bioinspired Polarization Imaging Sensors
described in Section VI. In order for this class of sensors to
live up to its full potential, signal processing algorithms
have to be developed with understanding of the underlying
structure of the sensor.
C. Processing to Visually Interpret Polarization
Since the human eye is polarization insensitive, displaying polarization information has posed a serious
hurdle and has impeded the advancement of polarization
research. Displaying the four Stokes parameters can often
lead to an overwhelming amount of information presented
to an end user. Measurements of the degree and angle of
polarization combine the information from the four Stokes
parameters and capture two important aspects of the light
field: the amount of polarization and the major axis of
oscillation, respectively. These two parameters can be
viewed separately or combined into a single image using
hue-saturation-value (HSV) transformation, greatly simplifying the presented information [63], [64]. Nevertheless,
displaying polarization information is still a challenging
problem. Further research on displaying polarization information is needed and will be a key factor for further
advancing the field of polarization and bridging polarization research to non-optics and non-engineering fields.
Because of the infancy of the bioinspired polarization
imaging sensor, a detailed opto–electronic performance
evaluation of these sensors has to be systematically developed. The performance of these sensors depends on many
Fig. 9. (a) Setup for electrical characterization. The integrating
sphere/aspheric lens combination creates a uniform field. (b) Setup for
polarization characterization, using the same water-immersion lens,
submerged in saline, as used for neural recording experiments.
Fig. 10. Measured output current from a pixel versus the number of
incident photons on the photodiode.
optical and electronic parameters. Light intensity impinging on the sensor plays a role, as the underlying sensor may
be limited by dark noise at low intensities and shot noise at
higher intensities. Wavelength influences the performance
of both the nanowire polarizers and the sensor, as the
sensor has a defined quantum efficiency, and the filters’
transmission properties are wavelength dependent. Focus
can also be an issue, as the possibility exists of divergent
light transmitting through a filter being detected through
a neighboring pixel. The aperture size (i.e., varying the
F-number) also impacts the divergence angle of the incident light. A detailed system performance evaluation,
such as the one proposed in [65], which includes an evaluation for different intensities, wavelengths, divergence,
and polarization states, can serve as an illustrative testing
The bioinspired sensor described in Section III-C was
given a series of electrical and optical tests to characterize
its performance. For the electrical tests, a set of narrowband light-emitting diodes (LEDs) (OPTEK OVTL01LGAGS) were placed flush to an integrating sphere
(Thorlabs IS200). The light was then collimated with an
aspheric condensing lens (Thorlabs ACL2520) before
reaching the sensor. The intensity of the light was changed
by altering the current through the LEDs with a constant
direct current (dc) power supply (Agilent E3631A). The
reference optical intensity was measured at the focal plane
of the sensor with a calibrated photodiode (Thorlabs
S120VC). Fig. 9(a) shows a diagram of the setup.
For the polarization characterization, to better calibrate
for the optics used in the neural recording experiments
presented in Section VI, the same integrating sphere/LED
combination was used as the light source. A rotating polarization element (Newport 10LP-Vis-B mounted in a Thorlabs
PRM1Z8 stage) was used to generate input linearly polarized
light of a known AoP. The sensor used a 10 waterimmersion lens (Olympus UMPLFLN10XW) submerged in a
glass dish of water to view the flat field generated from the
light source. Fig. 9(b) shows a diagram of the setup.
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Fig. 13. Optical response to four neighboring pixels to incident
linearly polarized light. As the angle of polarization of the
incident light is swept from 0 to 180 , the pixels follow
Malus’s law for polarization.
Fig. 11. SNR of the current-mode imaging sensor as a function of the
number of incident photons.
A. Electrical Characterization of the CMOS
Image Sensor
Fig. 10 shows the output current measured as a function of the incident light intensity. The current shows a
linear response with respect to the incident light, with
99% linearity in the range. This primarily results from the
current conveyor. The regulated cascode structure helps
eliminate channel length modulation while also maintaining a steady voltage reference. Fig. 11 shows the signalto-noise ratio (SNR) of the current-mode sensor. The
maximum SNR for our sensor is 43.6 dB, consistent with
the shot noise limit based on the pixel well-depth capacity.
Fig. 12 shows a histogram of image intensities. The fixedpattern noise for room light intensity is 0.1% from the
saturated level, comparable to voltage-mode imaging sensors. Also, due to the low currents and small array size, bus
resistance variation remains minimal.
B. Polarization Characterization of the Sensor
The sensor was tested for polarization sensitivity. To
improve polarization sensitivity, a Mueller matrix calibration approach was used [56]. Fig. 13 shows the pixel
response to polarized light after calibration. Malus’s law
(16) describes the intensity of light seen through two
polarizers offset at i degrees
I ¼ I0 cos2 i :
The pixels in the polarization sensor show nearly the same
response. The more uniform response after calibration also
manifests in a more linear AoP than the raw measurement,
as depicted in Fig. 14.
The emergence of bioinspired polarization imaging sensors
has enabled rapid advancements in several biomedical
areas. In this section, three biomedical applications are
Fig. 12. Histogram of all responses of pixels in the imaging
array to a uniform illumination at room light intensity.
The fixed pattern noise of the current-mode imaging sensor
Fig. 14. Measured angle of polarization as a function of the
incident light angle of polarization for our bioinspired
polarization imager.
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York et al.: Bioinspired Polarization Imaging Sensors
covered: 1) label-free optical neural recording; 2) soft
tissue stress analysis; and 3) in vivo endoscopic imaging for
flat lesion detection.
A. Optical Neural Recording With Polarization
Imaging sensors have greatly advanced the field of
neuroscience, especially through the use of fluorescent
imaging techniques. These techniques have enabled the
in vivo capture of neural activity from large ensembles of
neurons over wide spatial areas. With Ca2þ probes or
voltage-sensitive dyes, neuronal action potentials trigger a
corresponding optical change. This may change the optical
intensity, as when a photon is released upon a transition
from an excited state to a ground state. It may also change
the spectrum of light during neural activation [66].
Although fluorescent imaging has enabled a tremendous
success in the neuroscience field, a number of problems
impede further elucidation of neural activity. Many calcium markers require input excitation in the high-energy
ultraviolet (UV) spectrum, which can cause cell damage
over time. Additionally, fluorescent signals may be directly
toxic to the cell, or indirectly toxic by interacting with
nearby molecules during excitation [67]. Fluorescent
signals also decrease in intensity over time, after repeated
excitation and emission cycles, a process called photobleaching. Further, some structures in the cell intrinsically
fluoresce, overwhelming the measurement of any weaker
desired signals.
Two-photon excitation techniques mitigate some of
these deficiencies. This technique requires the simultaneous excitation of two low-energy photons to produce a
higher energy fluorescent photon. Two-photon excitation
typically focuses a high-power pulsed laser at the recording
image plane. Doing so reduces the background, as a signal
requires the simultaneous excitation of two photons, thus
increasing the SNR of the neural recording. Additionally,
tightly focusing the input beam to increase spot intensity
also significantly reduces background photobleaching.
Since the excitation wavelength is usually in the nearinfrared, two-photon techniques allow imaging deeper into
tissue than single-photon techniques that require UV.
These fluorescent techniques, however, can still result in
photobleaching over time, reducing the potential for longterm recording experiments.
Alternate optical techniques exist for measuring neural
activity. These methods capture the intrinsic changes of
light scattered from neural cells without the use of molecular reporters. Because these techniques rely only on intrinsic signals, they will not result in photobleaching after
repeated stimulus cycles, allowing for the possibility of
long recording periods. Since the signals are optical, they
also do not require the introduction of potentially destructive electrophysiology probes for measurement.
State-of-the-art techniques for using polarization to
measure neural activity are based on in vitro observation of
the birefringence change during stimulus. Isolated neural
cells are placed between two crossed polarizers and are
given an electrical stimulus while optical changes are recorded. During an action potential, the birefringence of the
neuron changes, thus causing an intensity change through
the crossed polarizer. Initial experiments were performed
on squid giant axons, with the SNR of early detection
methods limiting them to cultured neurons [68], [69].
More recent experiments have been able to go beyond
cultured neurons and use those extracted from lobster
(Homarus americanus) and crayfish (Orconectes rusticus) to
show birefringence change during action potentials [70].
Further experiments on the lobster nerve show that the
reflection of s-polarized light off the nerve through a
p-polarized filter also exhibits an optical intensity change
during an action potential propagation [71]. Since the birefringence changes, it is also possible to use circularly polarized light [72] to detect action potentials. However, all of
these in vitro methods have relied on isolated nerves, with
most of these methods employing only a single photodetector. A polarization sensor that has multiple detectors,
like the one presented here, could simultaneously capture
populations of neurons in vivo.
1) Model of Label-Free Neural Recording Using Polarization
Reflectance: From the theory covered in Section II-B, unpolarized light reflecting off of an object or tissue becomes
polarized based on the incident angle and index of
refraction. Therefore, if the incident lighting conditions
remain the same but the index of refraction changes, this
change manifests as a change in the reflected polarization
state of light. Neurons during an action potential show a
change in the index of refraction [73] and thus should also
show a change in the reflected polarization.
Detection of this change can be hindered in the presence of scattering, which causes a decrease in intensity in
the direction of propagation. Since neurons typically lay
within tissue, the small intrinsic changes in optical intensity that accompany an action potential can be lost.
Polarization signals can be more robust to scattering, as
evidenced by the use of polarization to see farther in hazy
environments [40]. This can be true in tissue as well, with
the polarization signal persisting longer through multiple
scattering events [74]. This means that detection of the
intrinsic polarization signal change might be possible.
If a neuron resides in tissue, then unpolarized light will
scatter on entrance to the tissue, reflect off of the neuron,
and scatter back toward the camera. The light will be partially polarized upon reflection, and although this polarized reflection will scatter during propagation back to the
sensor, as a polarized reflection it will be less affected by
the scattering, making detection possible with a real-time
2) Optical-Based Neural Recording With the Bioinspired
Polarization Imager: Fig. 15 shows the setup for optical
neural capture [75]. Optical neural activity was obtained
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Fig. 16. In vivo measurements from a population of neurons in the
locust’s antennal lobe. The locust antenna was exposed to series of ten
puffs of hexanol, octanol, or both combined, with each puff lasting for
4 s, followed by 56 s of no stimulus.
Fig. 15. Experimental setup for in vivo polarization-based optical
neural recording.
from the antennal lobe of the locust (Schistocerca
americana). The experiment required exposing the locust
brain. To ensure the locust’s viability, a wax cup formed a
watertight seal around the exposed area, holding a saline
solution [76]. To minimize motion artifacts, the locust was
immobilized on a floating optical table. Odors in airflow
were introduced to the locust through a plastic tube placed
around the antenna at a constant rate of 0.75 L/min. The
two odors used in the experiment, 1% hexanol and 1%
2-octanol, were both diluted in mineral oil. During the
stimulation period, odors were introduced at a rate of
0.1 L/min. The airflow is aspirated through a charcoal filter
at the same rate of flow around the antenna.
To image in vivo the locust’s olfactory neurons, the bioinspired current-mode CMOS polarization sensor was attached through a lens tube to an Olympus UMPLFLN10XW
water-immersion objective with 10 magnification. The
objective was placed in the saline solution and was focused
on the surface of the antennal lobe area of the brain closest
to the odor tube. As the focus is on the surface, the frame
rate of the sensor in these experiments (20 frames/s) allows
detection of the aggregate response of populations of surface neurons. The light source used for the optical recording was a custom circuit board containing ten 625-nm
center-wavelength LEDs, powered by a constant-current
power supply. A microcontroller synchronized the video
frames and a trigger used for introduction of the odor stimuli.
We used two different stimulation protocols for two
different experiments. In the first experiment, odors
were introduced for 4-s puffs at 60-s intervals. The odors
were interspersed as two puffs of hexanol, two puffs of
2-octanol, and two puffs of both odors combined. The
sequence was repeated five times. The second experiment
used the same 4-s puffs in 60-s increments, but in this case
the odors are introduced consecutively as ten hexanol
puffs, ten 2-octanol puffs, and ten combined puffs.
The data were filtered using a zero-phase bandpass
filter to eliminate high-frequency noise and low-frequency
drift. To improve the SNR of the neural signal, the data
were also spatially filtered from an 11 11 region of pixels
within the antennal lobe. Fig. 16 shows the results of the
second, 10-puff experiment. The average change for each
puff of hexanol was 0.38% 0.02%; 2-octanol, 0.15% 0.02%; and combined odors, 0.45% 0.03%. The
stronger response for hexanol over 2-octanol is consistent
with electrophysiological data. This trend persisted even
for highly interspersed sequences in the first, two-puff
experiment (Fig. 17): the average change for hexanol was
0.36% 0.06%; for 2-octanol, 0.16% 0.04%; and for
the combined odor, 0.54% 0.02%.
Fig. 18 presents 2-D maps of the neural activation pattern during stimulus presentation with the second protocol. The top row is the neural response to a hexanol puff,
the middle row is the neural response to a 2-octanol puff,
and the last row shows the neural response to a combination puff of both odors. The eight different images per
Fig. 17. In vivo measurements from a population of neurons in the
locust antennal lobe to highly interspersed odors during the first
experiment, comprising two puffs per odor exposure.
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York et al.: Bioinspired Polarization Imaging Sensors
Fig. 18. Spatial activation of neural activity across the locust’s
antennal lobe. Each 2-D map is a dorsal view, with the left as
lateral, right as medial, top as caudal, and bottom as rostral.
The eight images per row represent neural activity at particular time
intervals indicated at top of each column. Refer to Video 3 in the
supplementary material.
row depict the neural activity at a particular time interval
indicated at the top of each image.
The activation maps show a response that spreads from
the portion of the lobe closest to the antenna, and the
source of odor, outward through the rest of the antennal
lobe. The images show some similarity to the response
dynamics observed in the population neural activity. The
maps show the measurement of the scattering of light
changes from the activation of populations of neurons. It
has been previously shown that these changes are
proportional to the change in voltage potential during
activation [69].
This new class of bioinspired polarization imaging
sensors is opening unprecedented opportunities in the advancement of the knowledge in neuroscience. The possibility of recording neural activity from a large population
of neurons with high temporal fidelity can help in understanding how information is processed in the olfactory
system or other sensory systems in the brain. Such questions as how the primary coding dimensions, time and
space, are used in biological signal processing can possibly
be answered. These imaging sensors can ultimately lead to
implantable neural recording devices based on measuring
the optical intrinsic signals. The monolithic integration of
optical filters with CMOS imaging arrays makes this sensor
architecture the only viable solution for implantable devices in animal models, allowing the study of neural activity in awake, freely moving animals.
structure–function relationships through correlation of
alignment data with measured mechanical properties
under different loading conditions. Traditional measurements involve applying a fixed amount of force to the
tissue and then rotating crossed polarizers on either side of
the tissue. The structure of collagen fibers (i.e., long and
thin) creates optical birefringence along the direction of
the alignment of each fiber, which causes transmitted
illumination through the crossed polarizers. Rotation of
the crossed polarizers through 180 enables detection of
the angles of maximum and minimum transmitted illumination, which correspond to the alignment direction of
the collagen fibers.
This imaging method is a standard technique for analysis of tissue alignment; however, the time required for
rotation of the polarizers precludes real-time measurement
of dynamically loaded tissue. Further, errors are introduced using this method, as the force applied to the tissue
may not remain constant during the time of rotation (and
image acquisition). This leads to inaccuracy in the polarization measurements for the collagen fiber alignment and
The bioinspired polarization imager does not require
the rotation of any polarization analyzing components and
thus can be used to make real-time (i.e., 30 frames/s)
measurements of dynamically loaded tissue. This modality
requires the use of transmitted circularly polarized light
through the tissue, which has a DoLP of 0. The birefringence of the tissue introduces a phase delay between
the transmitted x=y field components, causing the
circularly polarized light to become more linearly polarized as it passes through the tissue. In fact, the amount of
phase retardance between x and y is the inverse sine of
the DoLP
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðsin2 2 þ cos2 2Þ sin2 S21 þ S22
DoLP ¼
¼ sin :
The AoP of the transmitted light corresponds to the alignment of the tissue. The alignment of the fiber corresponds to the fast axis of a linear retarder. Thus, if the
tissue is rotated with respect to the sensor, computing the
AoP shows the alignment of the fiber
B. Real-Time Measurement of Dynamically Loaded
Soft Tissue
The bioinspired polarization imager allows for realtime measurement of dynamically loaded tissue [77]. Measuring the alignment of collagen fibers gives insight into
the anisotropy and homogeneity of the tissue’s microstructural organization and enables characterization of
AoP ¼
ð90 2Þ
¼ 45 :
Since the AoP shows the alignment, and the DoFP polarimeter captures the AoP at real-time speeds, the DoFP
polarimeter is also capable of computing the spread in the
AoP, which shows the spread in the fiber alignment. The
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Fig. 19. Bovine flexor tendon under cyclic load. (Left) DoLP (top) and change in retardance (bottom) over time. (Right) AoP (top), which
corresponds to the collagen alignment, and spread in alignment angle (bottom). Refer to Video 4 in the supplementary material.
AoP spread is an indicator of the relative strength or
weakness of the tissue, as smaller spreads in the fiber
alignment generally correlate with stronger tissue along
the principal fiber direction.
This technique was evaluated using a thin section
(300 m) of bovine flexor tendon, which was selected as
a representative soft connective tissue of highly aligned
collagen fibers. The tendon was secured to tissue clamps
and loaded using a computer-controlled linear actuator,
which precisely measures the force applied to the tendon
using a six-degree-of-freedom sensor. A linear polarizer
was placed at 45 with respect to a broadband quarterwave plate to generate circularly polarized light. We used a
standard 16-mm fixed-focus lens with our DoFP polarimeter [6] to measure the light transmitted through the
tendon. The tissue was cyclically loaded at 1 Hz, with a
displacement amplitude of 1 mm.
Fig. 19 shows an example of the tendon under strain.
The images on the left demonstrate that the DoLP is
maximal when the tissue is subject to the highest force. The
image on the right shows the AoP with the angle mapped
to the hue in the HSV color space. The graphs at the bottom
chart the change in retardance (left) and the spread of the
AoP (right) in the central portion of the tissue. Both curves
follow the 1-Hz loading, showing that the method is capable
of real-time capture of the tissue dynamics.
This imaging method opens up the possibility of using
even more complex loading protocols that require highspeed, real-time measurements (e.g., step and impulse
forces), much faster cyclic loading, or high-speed observation of tissue failure. This can lead to better understanding of the mechanical properties of connective
tissues, yield insight into structure–function relationships
in health and disease, and provide guidance for novel
development of orthopedic structures and devices.
C. Real-Time Endoscopy Imaging of Flat Cancerous
Lesions in a Murine Colorectal Tumor Model
With The Bioinspired Polarization Imager
Flat depressed cancerous and precancerous lesions in
colitis-associated cancer have been associated with poor
clinical outcomes. The current gold standard diagnostic
regime involves using a color endoscope that is incapable
of capturing flat lesions, which are abundant in this patient
population. With about 50%–80% of these lesions going
undetected using color endoscopy, there is much room for
improvement. Use of targeted molecular markers in optical imaging [78] has demonstrated that they have a unique
ability to accumulate in both precancerous and cancerous
lesions, making them a strong candidate for visual enhancement. The major drawback is the uncertainty in the
lack of signal in other visually suspicious regions that could
be dysplastic or cancerous. Investigation and validation of
these require biopsy and external analysis.
Polarization provides a possible complementary channel to aid in online and in vivo diagnosis. Since dysplastic
and cancerous regions are structurally different from those
of normal tissue, observation of the reflected polarization
signature could provide detection that does not require
biopsy and histological analysis. Cancerous and dysplastic
tissues typically contain higher densities of scattering
agents, causing them to exhibit a greater level of depolarization compared with neighboring healthy tissue. Detecting this polarization in vivo during an endoscopy is made
Vol. 102, No. 10, October 2014 | Proceedings of the IEEE
York et al.: Bioinspired Polarization Imaging Sensors
capabilities for early detection of cancerous tissues in
humans. To achieve this goal, advancements in nanofabrication techniques and nanomaterials, in signal processing and information display, and in system-level
instrumentation development would be required, together
with a multidisciplinary approach to improve diagnosis of
cancerous and precancerous lesions.
Fig. 20. DoLP image from in vivo endoscopy of mouse colon.
AT: adenomatous tumor; PP: Peyer’s patch; UT: uninvolved tissue.
Refer to Video 5 in the supplementary material.
possible by the development of a sensor capable of realtime measurement of polarization, such as the bioinspired,
fully integrated sensors showcased here.
We have tested the use of a complementary
fluorescence/polarization endoscope in mice with colorectal tumors induced through the azoxymethane–dextran
sodium sulfate (AOM–DSS) protocol [19]. We topically
applied LS301, a fluorescent dye with emission in the nearinfrared, on suspect regions in the mouse colon. These
regions were visually inspected using a Karl Storz Hopkins
rod endoscope, to which we could attach a fluorescencesensitive CCD camera (Fluoro Vivo) or the bioinspired
polarization sensor [6]. Guided by the fluorescent signals,
we used the polarization sensor to image suspected regions
of the colon. In the example shown in Fig. 20, we were
able to detect the tumor region by its lower DoLP compared with both the Peyer’s patch and uninvolved tissue.
From the various samples, we found that both tumors
(0.0414 0.0142) and flat lesions (0.0225 0.0073)
showed lower DoLP signatures than nearby surrounding
uninvolved tissue (0.0816 0.0173 and 0.0924 0.0284,
respectively). These signatures were verified using fluorescence, and further validated with histology.
The integration of nanowire polarization filters with an
array of CMOS imaging elements generates a compact
imaging system capable of providing polarization information with high spatial and temporal fidelity. This compact polarization imaging sensor is the only one to date
that can be integrated in the front tip of flexible endoscopes. Such integration can lead to unprecedented imaging
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Timothy York (Member, IEEE) received the B.S.E.E.
and M.S.E.E degrees from Southern Illinois University, Edwardsville, IL, USA, in 2002 and 2007,
respectively. Currently, he is working toward the
Ph.D. degree in computer engineering at Washington University in St. Louis, St. Louis, MO, USA.
He worked as a Computer Engineer for the
U.S. Air Force from 2006 to 2009. His current
work focuses on polarization image sensors and
their applications, especially in the field of
biomedical imaging.
Mr. York is a member of the International Society for Optics and
Photonics (SPIE).
Samuel B. Powell is working toward the Ph.D.
degree in the Department of Computer Science
and Engineering, Washington University in St. Louis,
St. Louis, MO, USA.
His work has focused on the calibration of
division-of-focal-plane polarimeters and, more
recently, the development of the hardware and
software for an underwater polarization video
camera for marine biology applications.
Shengkui Gao received the B.S. degree in electrical engineering from Beihang University, Beijing,
China, in 2008 and the M.S. degree in electrical
engineering from the University of Southern
California, Los Angeles, CA, USA, in 2010. Currently, he is working toward the Ph.D. degree in
computer engineering at Washington University
in St. Louis, St. Louis, MO, USA.
His research interests include imaging sensor
circuit and system design, mixed-signal VLSI
design, polarization related image processing and optics development.
Lindsey Kahan is an undergraduate at Washington University in St.
Louis, St. Louis, MO, USA, studying mechanical engineering and materials
Tauseef Charanya received the B.S. degree in
biomedical engineering from Texas A&M University,
College Station, TX, USA, in 2010. Currently, he is a
biomedical engineering graduate student at
Washington University in St. Louis, St. Louis, MO, USA.
He is a cofounder of a medical device incubator
at Washington University in St. Louis named IDEA
Labs. His research interests include endoscopy,
surgical margin assessment tools, and fluorescence and polarization microscopy methods.
Mr. Charanya is a member and serves as the President of the WU
Chapter of the International Society for Optics and Photonics (SPIE).
Debajit Saha received the M.S degree from the
Indian Institute of Technology, Bombay, India and
the Ph.D. degree for his work on the role of feedback loop in visual processing from Washington
University in St. Louis, St. Louis, MO, USA.
He is currently a Postdoctoral Fellow at the
Biomedical Engineering Department, Washington
University in St. Louis, working on understanding
the functional roles of neural circuitry from
systems point of view. His research interests
include olfactory coding, quantitative behavioral assay, and application
of rules of biological olfaction in biomedical sciences.
Nicholas W. Roberts, photograph and biography not available at the
time of publication.
Thomas W. Cronin received the Ph.D. degree
from Duke University, Durham, NC, USA, in 1979.
He is a Visual Ecologist, currently in the
Department of Biological Sciences, University of
Maryland Baltimore County (UMBC), Baltimore,
MD, USA, where he studies the basis of color and/
or polarization vision in a number of animals
ranging from marine invertebrates to birds and
whales. He spent three years as a Postdoctoral
Researcher at Yale University, New Haven, CT,
USA, before joining the faculty of UMBC.
Proceedings of the IEEE | Vol. 102, No. 10, October 2014
York et al.: Bioinspired Polarization Imaging Sensors
Samuel Achilefu received the Ph.D. degree in
molecular and materials chemistry from University of Nancy, Nancy, France and completed
his postdoctoral training at Oxford University,
Oxford, U.K.
He is a Professor of Radiology, Biomedical
Engineering, and Biochemistry & Molecular Biophysics at Washington University in St. Louis,
St. Louis, MO, USA. He is the Director of the Optical
Radiology Laboratory and of the Molecular Imaging Center, as well as Co-Leader of the Oncologic Imaging Program of
Siteman Cancer Center.
Baranidharan Raman received the B.Sc. Eng.
degree (with distinction) in computer science from
the University of Madras, Chennai, India, in 2000
and the M.S. and Ph.D. degrees in computer
science from Texas A&M University, College
Station, TX, USA, in 2003 and 2005, respectively.
He is an Assistant Professor with the Department of Biomedical Engineering, Washington
University, St. Louis, MO, USA. From 2006 to
2010, he was a joint Post-Doctoral Fellow with the
National Institutes of Health and the National Institute of Standards and
Technology, Gaithersburg, MD, USA. His current research interests
include sensory and systems neuroscience, sensor-based machine
olfaction, machine learning, biomedical intelligent systems, and dynamical systems.
Dr. Raman is the recipient of the 2011 Wolfgang Gopel Award from the
International Society for Olfaction and Chemical Sensing.
Spencer P. Lake received the Ph.D. degree in bioengineering from the University of Pennsylvania,
Philadelphia, PA, USA.
He is an Assistant Professor in Mechanical
Engineering & Materials Science, Biomedical Engineering and Orthopaedic Surgery at Washington
University in St. Louis, St. Louis, MO, USA, and
directs the Musculoskeletal Soft Tissue Laboratory. His research focuses on biomechanics of soft
tissues, with particular interest in the structurefunction relationships of orthopaedic connective tissues.
Viktor Gruev received the M.S. and Ph.D. degrees
in electrical and computer engineering from The
Johns Hopkins University, Baltimore, MD, USA, in
May 2000 and September 2004, respectively.
After finishing his doctoral studies, he was a
Postdoctoral Researcher at the University of
Pennsylvania, Philadelphia, PA, USA. Currently,
he is an Associate Professor in the Department of
Computer Science and Engineering, Washington
University in St. Louis, St. Louis, MO, USA. His
research interests include imaging sensors, polarization imaging,
bioinspired circuits and optics, biomedical imaging, and micro/
Justin Marshall, photograph and biography not available at the time of
Vol. 102, No. 10, October 2014 | Proceedings of the IEEE
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