Automatic Quantification of Pupil
Dilation under Stress
CADDLab @ UNC
Julien Jomier, Erwann Rault, and Stephen R. Aylward
Computer Aided-Diagnosis and Display Lab - University of North Carolina at Chapel Hill
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.
We are particularly interested in testing the hypothesis that dark
adaptation is slowed proportional to the amount of stress that an
individual has experienced.
Coarse Pupil and Iris Segmentation
1) Eye Localization
- 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.
- Statistics of the iris are
estimated on the left and right
part of the pupil.
- Only
the
radius
is
regions used to
optimized. The center of the evaluate statistics
iris is assumed to be the
of the iris
center of the pupil.
- Calculate the optimal linear discriminant between pupil and
iris color classes to compute a pupil-likelihood image (TopRight). 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
Resulting precise segmentation of the
[3] (Bottom-Right)
pupil via active contours
Eye localization using color statistics of the pupil (middle) from the original
image (left) resulting to a definition of two regions of interest (right)
2) Coarse Pupil Segmentation
The pupil is segmented in three steps.
Blue  Re d     0
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.
Ar  r , X   Ar , X 
f(r,X) 
2  r  r
Resulting
segmentation of
the iris
Precise Pupil Segmentation
Results
- Training : 5 left eyes from different subjects.
- Testing: 20 eyes from 10 different patients.
- Comparison of the automated algorithm with handsegmentation of 5 raters shows equal accuracy.
- Automatic segmentation takes less than 1 minute per image
(2 times faster than manual segmentation).
- Fully automatic
Average
Segmentation
 Error

Raters
Computer
35010
34780
1.94%
2.41%
Max
Error
6.76%
5.80%
Average and Maximum error in %
Introduction
8
7
6
5
4
3
2
1
0
 Error
Max Error
Comparison with hand-segmentation of the pupil by 5 raters on 10
images.
References
Plot of the metric for a radius r=3
Resulting segmentation of the
pupil
3) Coarse Iris Segmentation
The segmentation of the iris is done with the same technique as the
pupil.
Computer-Aided Diagnosis and Display Lab
Department of Radiology, UNC @ Chapel Hill
[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
April 2004