Texture Analysis for the
evaluation
of human irises reproduction in
ocular prostheses and colored
contact lenses
Jorge Herrera, Meritxell Vilaseca, Montserrat Arjona, Jochen
Düll, Jaume Pujol.
Centre for Sensors, Instruments and Systems
Development (CD6).
Optics and Optometry Department,
Technical University of Catalonia (UPC)
OUTLINE
• INTRODUCTION
• PREVIOUS WORK:
– EXPERIMENTAL SETUP AND SAMPLES
– AVERAGE COLOR STUDY
• TEXTURE METHODS
• RESULTS
• CONCLUSIONS
2
INTRODUCTION
• Motivation:
- Increase the knowledge of color
distribution in human irises through the use
of a CCD camera and multispectral
techniques.
- Study of human irises reproduction made
by ocular prostheses and colored contact
lenses
• Objectives:
- Implement the different tools necessary to
assess the texture of human irises.
- Compare textural features among human
irises, ocular prostheses and contact lenses.
3
PREVIOUS WORK: Experimental
setup
Relative Spectral Sensitivity
RGB ACQUISITION
R Channel
G Channel
B Channel
1.0
0.8
0.6
0.4
0.2
0.0
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600
650
700
Wavelength (nm)
Collaboration with La Universidad de Granada, M. Melgosa and R. Huer
and F. Imai from Samsung Company
Vilaseca et.al. Applied Optics, 2008.
4
PREVIOUS WORK: samples and
pixelwise conversion from raw digital
levels to color data.
Samples: 106 Irises, 68
Prostheses and 17
colored contact Lenses.
Human irises
0.35
RGB
Acquisition
Spectral
Reconstruction:
Pseudo-inverse
(PSE)
Spectral reflectance
0.30
0.25
0.20
0.15
0.10
0.05
0.00
400
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520
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680
720
Wavelength (nm)
Illuminant
Tristimuli values
XYZ
CIE L*a*b*
Coordinates
5
760
PREVIOUS WORK: average color
assessment
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PREVIOUS WORK: average color
assessment
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TEXTURE METHODS: image
segmentation process
• The segmentation
process is divided
in four main
steps:
1.Pupil
4.Detection
2.3.Occlusion
Border
detection
ofdetection
detection
specular
from Iris
reflections
between
-Variable
eyelashes
and
sclera
thresholding.
Similar
and eyelids
zone
procedure to
-Blob
-one
Onanalysis.
border
the-Kirsch
used
edge
point
in -Eccentricity
enhancement
pupil
detection
and
solidity and
detection
filtering
line like
measurements
-Ellipse
Hough
drawing
transform
Screen shot of the application in Matlab
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8
TEXTURE METHODS: Statistical approach to
texture description and CIEDE2000 Color
difference images
First order texture measures are statistics calculated from the original image
values, like variance, and do not consider pixel neighbour relationships.
Second order measures consider the relationship between groups of two
pixels in the original image.
Third and higher order textures (considering the relationships among three or
more pixels)
L*
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100
a*
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250
b*
50
500
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100
550
350
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400
200
Mean
L*a*b*
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350
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CIEDE2000
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700
TEXTURE METHODS: First-order
statistics
• First-order statistics from the image
histogram
Pi
Entropy
N 1
Ep   Pi log 2  Pi 
i 0
Energy
N 1
En   Pi 2
i 0
1 . . . . . i . . . . . . N
3rd central
moment
N 1
3    i  m  Pi
i 0
3
N 1
m   iPi
i 0
10
TEXTURE METHODS: Co-occurrence
matrices
Co-occurrence matrices (equivalently secondorder statistics, Haralick’s descriptors)
This matrix is a tabulation of how often
different combinations of pixel brightness
values (grey levels) occur in an image.
Parameters: Levels(matrix size), distance
and angle
1 . .
N 1
Entropy
Energy
Contrast
Ep    Pi , j log2 Pi , j 
i , j 0
En 
C
N 1
N 1
2
i, j
 P i  j 
i , j 0
i, j
.
.
. . N
1
.
.
i
P
i , j 0
. j
.
.
2
N
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TEXTURE METHODS: test of
classification
• Linear discriminant analysis
-Discriminat functions are the linear
combinations of variables that better
separates the groups.
RESULTS
TEXTURE ANALYSIS WITH FIRST-ORDER STATISTICS.
Linear discriminant analysis based on the first-order statistics
(Classification of samples according to texture)
Correct irises (%)
Correct prostheses (%)
Correct C. Lenses (%)
68.4
43.3
64.7
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RESULTS
TEXTURE ANALYSIS WITH SECOND ORDER STATISTICS.
Linear discriminant analysis based on the second-order
statistics (Classification of samples according to texture)
Correct irises (%)
Correct prostheses (%)
Correct C. Lenses (%)
88.4
82.1
100.0
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CONCLUSIONS
- Different tools from statistics and image
processing were implemented for the analysis of
the texture of the samples
- It was shown that there exist differences in
texture between samples, so that classification
is possible to distinguish them according to their
textural features.
• These results may be helpful for companies that
produce prostheses and colored contact lenses.
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Acknowlegments
This work was supported by the Ministry of Science
and Innovation through the Project DPI2008-06455C02-01.
Jorge Herrera gratefully acknowledges the support of the
Commissioner for Universities and Research of the Department of
Innovation, Universities and Enterprise of the Generalitat of
Catalonia and the European Social Fund
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¡Thanks for your
attention!
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