Computer-Aided Diagnosis and Display Lab Department of Radiology, UNC @ Chapel Hill CADDLab @ UNC...
1
Computer-Aided Diagnosis and Display Lab Department of Radiology, UNC @ Chapel Hill CADDLab @ UNC Julien Jomier, Erwann Rault, and Stephen R. Aylward Computer Aided-Diagnosis and Display Lab - University of North Carolina at Chapel Hill Automatic Quantification of Automatic Quantification of Pupil Dilation under Stress Pupil Dilation under Stress Introduction Results References Coarse Pupil and Iris Segmentation Precise Pupil Segmentation April 2004 [1] D. H. Ballard, “Generalized hough transform to detect arbitrary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 111–122, 1981 [2] M Styner and G. Gerig, “Evaluation of 2d/3d bias correction with 1+1es-optimization,” Technical Report BIWI-TR-179 [3] Ross T. Whitaker, “Algorithms for implicit deformable models,” in Fifth International Conference on Computer Vision. IEEE, 1995, IEEE Computer Society Press [4] Insight Software Consortium, “The insight toolkit: Segmentation and Registration toolkit,” http://www.itk.org Segmentati on Averag e Error Max Error Raters 35010 1.94% 6.76% Computer 34780 2.41% 5.80% r r X r A X r r A f(r,X) 2 , , Eye localization using color statistics of the pupil (middle) from the original image (left) resulting to a definition of two regions of interest (right) We seek to automate the measurement of pupil and iris areas from color digital photos so as to calculate the ratio of those areas as a measure of the amount that an individual has dark- adapted. 1) Eye Localization Resulting segmentation of the iris Comparison with hand-segmentation of the pupil by 5 raters on 10 images. Plot of the metric for a radius r=3 - Statistics of the iris are estimated on the left and right part of the pupil. - Only the radius is optimized. The center of the iris is assumed to be the center of the pupil. - Sub sampling is performed to improve computation speed. - Statistics filter is applied to extract pixels that have the red component higher than the blue component (Red – Blue < ) defining two regions of interest around each eye. 2) Coarse Pupil Segmentation The pupil is segmented in three steps. 1) The pixels that satisfy the equation are set to 1 and 0 otherwise to produce a binary image. 2) Hough Transform [1] is used to approximate the center and radius of the best fitting circle in the binary image. 3) We apply a model-to-image registration [Aylward 2001] using the 1+1 evolutionary optimizer [2]. We define the metric f of the fit of the circle with the binary image. 0 Re d Blue - Training : 5 left eyes from different subjects. - Testing: 20 eyes from 10 different patients. - Comparison of the automated algorithm with hand-segmentation of 5 raters shows equal accuracy. - Automatic segmentation takes less than 1 minute per image (2 times faster than manual segmentation). - Fully automatic regions used to evaluate statistics of the iris Resulting precise segmentation of the pupil via active contours Resulting segmentation of the pupil The segmentation of the iris is done with the same technique as the pupil. 3) Coarse Iris Segmentation - Calculate the optimal linear discriminant between pupil and iris color classes to compute a pupil-likelihood image (Top-Right). We seek the boundary between the iris and the pupil that is emphasized by that likelihood image. - Threshold the likelihood image to form a binary image such that every pixel with a likelihood greater or equal to zero is set to one. We then use morphological operations to reduce noise (Bottom-Left). - Active contours segmentation [3] (Bottom-Right) 0 1 2 3 4 5 6 7 8 A verage and M axim um errorin % Error Max Error We are particularly interested in testing the hypothesis that dark adaptation is slowed proportional to the amount of stress that an individual has experienced.
-
date post
21-Dec-2015 -
Category
Documents
-
view
215 -
download
0
Transcript of Computer-Aided Diagnosis and Display Lab Department of Radiology, UNC @ Chapel Hill CADDLab @ UNC...
Computer-Aided Diagnosis and Display LabDepartment of Radiology, UNC @ Chapel Hill
CADDLab @ UNC
Julien Jomier, Erwann Rault, and Stephen R. AylwardComputer Aided-Diagnosis and Display Lab - University of North Carolina at Chapel Hill
Automatic Quantification of Pupil Automatic Quantification of Pupil Dilation under StressDilation under Stress
Introduction
Results
References
Coarse Pupil and Iris Segmentation
Precise Pupil Segmentation
April 2004
[1] D. H. Ballard, “Generalized hough transform to detect arbitrary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 111–122, 1981
[2] M Styner and G. Gerig, “Evaluation of 2d/3d bias correction with 1+1es-optimization,” Technical Report BIWI-TR-179
[3] Ross T. Whitaker, “Algorithms for implicit deformable models,” in Fifth International Conference on Computer Vision. IEEE, 1995, IEEE Computer Society Press
[4] Insight Software Consortium, “The insight toolkit: Segmentation and Registration toolkit,” http://www.itk.org
SegmentationAverage
ErrorMax
Error
Raters 35010 1.94% 6.76%
Computer 34780 2.41% 5.80%
rr
XrAXrrAf(r,X)
2
,,
Eye localization using color statistics of the pupil (middle) from the original image (left) resulting to a definition of two regions of interest (right)
We seek to automate the measurement of pupil and iris areas from color digital photos so as to calculate the ratio of those areas as a measure of the amount that an individual has dark-adapted.
1) Eye Localization
Resulting segmentation of
the iris
Comparison with hand-segmentation of the pupil by 5 raters on 10 images.
Plot of the metric for a radius r=3
- Statistics of the iris are estimated on the left and right part of the pupil.
- Only the radius is optimized. The center of the iris is assumed to be the center of the pupil.
- Sub sampling is performed to improve computation speed.- Statistics filter is applied to extract pixels that have the red component higher than the blue component (Red – Blue < ) defining two regions of interest around each eye.
2) Coarse Pupil Segmentation
The pupil is segmented in three steps. 1) The pixels that satisfy the equation are set
to 1 and 0 otherwise to produce a binary image.2) Hough Transform [1] is used to approximate the center and
radius of the best fitting circle in the binary image. 3) We apply a model-to-image registration [Aylward 2001]
using the 1+1 evolutionary optimizer [2]. We define the metric f of the fit of the circle with the binary image.
0
Re
dBlue
- Training : 5 left eyes from different subjects.
- Testing: 20 eyes from 10 different patients.
- Comparison of the automated algorithm with hand-segmentation of 5 raters shows equal accuracy.
- Automatic segmentation takes less than 1 minute per image (2 times faster than manual segmentation).
- Fully automatic
regions used to evaluate statistics
of the iris
Resulting precise segmentation of the pupil via active contours
Resulting segmentation of the pupil
The segmentation of the iris is done with the same technique as the pupil.
3) Coarse Iris Segmentation
- Calculate the optimal linear discriminant between pupil and iris color classes to compute a pupil-likelihood image (Top-Right). We seek the boundary between the iris and the pupil that is emphasized by that likelihood image.- Threshold the likelihood image to form a binary image such that every pixel with a likelihood greater or equal to zero is set to one. We then use morphological operations to reduce noise (Bottom-Left). - Active contours segmentation [3] (Bottom-Right)
0
1
2
3
4
5
6
7
8
Av
era
ge
an
d M
ax
imu
m e
rro
r in
%
Error Max Error
We are particularly interested in testing the hypothesis that dark adaptation is slowed proportional to the amount of stress that an individual has experienced.
