DEVELOPING A PUPILLOMETER

Gonçalo Leal

, Pedro Vieira

and Carlos Neves

Department of Physics, Science and Technology Faculty, New University of Lisbon, Portugal

Institute of Biophysics and Biomedical Engineering, Science Faculty, University of Lisbon, Portugal

Department of Physiology, Institute of Molecular Medicine, School of Medicine, Lisbon University, Portugal

Keywords: Pupil, Pupillometer, Medical Instrumentation, Ophthalmology.

Abstract: This project presents stable and robust optical equipment for detecting the area of the pupil and its variation

on a temporal scale. An algorithm was developed to detect the pupil contour, implemented in a simple,

intuitive and user friendly interface programme. Using the equipment specifically developed, measurements

were taken of the area, perimeter, horizontal diameter and vertical diameter. After a statistical and

comparative study, it was possible to reach conclusions regarding the general dimensions of the pupil, its

variation prompted by a given stimulus and the clinical viability of the equipment concerned.

1 INTRODUCTION

The area of the pupil changes in response to the

variation in light intensity in the retina, with a view

to assisting the optimizing of visual perception. In

dim light, pupil dilation (midriasis) is an effective

way to maximize the number of photons reaching the

retina, which in turn activates adaptive mechanisms

to low light intensity. When exposed to bright light,

miosis causes an adequate reduction in the intensity

of light in the retina, acting as immediate response to

the mechanisms of adapting to intense light (Kardon,

2003). The study of the way the pupil changes its

size is of relevant clinical interest, for it acts as an

objective indicator of the retina’s sensitivity to light

and, as a result, of the optical nerve functionality.

The state of a person’s pupil allows for several

diseases to be diagnosed, among which sleep

disturbances (narcolepsy), photophobia,

schizophrenia (pharmacological reaction), Adie’s

Syndrome, Alzheimer’s, narcotic addiction, among

others.

2 STATE OF THE ART

Nowadays, Pupillometry is mostly based in

computers. This technology, based on image

processing, numerically analyses the pupil features.

The sensitivity of existing systems varies much and

depends to a major degree on the spatial and

temporal resolution of the acquisition devices and

also on the algorithms used. Pupillometers differ also

in portability and applicability. The majority of

current Pupillometry devices use algorithms that are

based on physical models and that use a circle or an

ellipsoid as an approach to the pupil contour (Kim et

al, 2004).

Recent studies have been made using Fourier

series to determine the pupil contour (Rakshit and

Monro, 2007).

3 MATERIALS

3.1 Material Used

The current system is based on a LE175C

LUMENERA camera that is connected, via Ethernet,

to a personal computer (figure 1). A floodlight of

500W was used as external illumination and as a

stimulator of the subject’s pupil. The camera is

placed in a mechanical arm attached to a table. The

arm can be moved vertically and horizontally in a

small area (figure 2a). The subject’s head is

positioned in a mechanical support that can be

moved vertically (figure 2b).

314

Leal G., Vieira P. and Neves C. (2009).

DEVELOPING A PUPILLOMETER.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 314-319

DOI: 10.5220/0001780403140319

 SciTePress

3.2 System Concept



Figure 1: First version of the Pupillometer System

designed in AutoCAD.



Figure 2: (a) Mechanical arm that moves the camera

horizontally and vertically. (b) Mechanical device where

the subject places his/her head. This device allows vertical

movement.

4 METHODS

4.1 Algorithm

The Matlab programme was chosen to develop all

the software required for the system, since it is a

very resourceful software and has very good imaging

tools and functions.

An algorithm was developed based on the

concept of Intensity Threshold. It starts with an

initial point (calculated by a secondary algorithm

shown in figures 3 and 4) and traces lines to every

direction separated by a 5 degree angle (LI,

Dongheng and Derrik J. Parkhurst, 2006). It then

compares the intensity of gray levels of consecutive

points along the traced line. If the variation of values

is bigger than the Threshold value, the algorithm

stops and defines a point that characterizes the pupil

contour. Then a second line is traced using the

previously calculated point as the new start point,

but in the opposite direction, in order to calculate the

opposite contour point and so speed up the time of

measurement. Once all points are defined, there are

specified algorithms that calculate the area,

perimeter, horizontal diameter and vertical diameter

of the image. The value of Threshold can be

manually inserted or can be calculated by two

algorithms developed for that purpose (Square

Threshold and Circle Threshold).



Figure 3: Analysis of gray intensity in the image. We can

see the pupil zone featured by low levels of intensity. This

is how the programme calculates the initial point for the

beginning of the cycle.



Figure 4: In order to improve the analysis shown in figure

3 we can plot the intensity of an image in a 3D mesh plot.

The pupil is shown in dark blue, since it has the lowest

intensity gray levels. Both images are the same, but image

b) is the result of a) with a Low Pass filter applied. This

feature reduces the error because the eyelashes are also

dark and could be misunderstood as an area of the pupil by

the algorithm.

4.2 Graphic User Interface (GUI)

To put all the algorithms together, a user friendly

(a)

(b)

(a)

(b)

DEVELOPING A PUPILLOMETER

315



interface was developed in Matlab, which acquires

all data by filming the eye of the subject. This

interface also measures pupil dynamics by

processing large amounts of images and determines

the statistical data of the results.

There are three main panels: data acquisition

panel, pupil detection panel and statistics panel.

The first panel (figure 5) shows the images that

are being stored in real time. We can also edit the

subject’s demographic data and the calibration data.

There are event buttons that store the time when a

stimulus is applied to the subject (the better to

analyse the statistical data).





Figure 5: Data Acquisition Panel.

The second panel (figure 6) is used to measure the

pupil dynamics during a period of time. There is a

menu where the user can easily calculate the

threshold value for automatic detection (figure 7).



Figure 6: Pupil Detection Panel.

 

Figure 7: a) Square threshold (compares the intensity gray

levels of 5 pupil points to 5 iris points). b) Circle

Threshold (compares a torus of points of the pupil to a

torus of points of the iris, mainly in the pupil/iris border.

The function of the third panel (figure 8) is to view

all the data statistics in two ways: by plots and by

tables. The user can also view the standard deviation

of each variable and save all data to a .txt file that

can easily be exported to other programmes used in

Fourier and Wavelet analysis.



Figure 8: Statistics Panel.

Outside the three panels, the interface has three

buttons, one for exiting the programme, a second one

to view the help file (a .pdf document with the user

guide of the software) and a third to consult the IDs

of all subjects in the database.

4.3 Data Acquisition

For this preliminary study, the right eye of nine

individuals was measured in a dark room where the

only light source was a 500W floodlight. Each

subject was illuminated with a bright light, so that

the pupil’s accommodation over time could be

detected (first the pupil contracted and then the pupil

dilated over time). Each subject’s head was

positioned at a distance of about 25cm from the

camera. The light stimulus was applied to the subject

and didn’t change during the measurements.

In order to remove the reflexes from the subjects’

eye, no direct illumination was used.

A hundred images were taken for each subject

(for about 10-15 seconds, depending on the speed of

image storing, which is independent of the amount

of data in the camera and also with the processing

speed of the computer used).

Table 1: Acquired data statistical information.

Subject Data Mean

Standard

Deviation

Test 1

Area

14444.80 1509.91

Perimeter

493.35 35.20

Horizontal

Diameter

143.10 7.00

Vertical

Diameter

140.57 7.08

Test 2

Area

11062.80 1508.11

Perimeter

482.12 49.79

Horizontal

Diameter

127.84 6.93

Vertical

Diameter

135.20 10.01

(a)

(b)

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

316

Table 1: Acquired data statistical information (Cont).

Test 3

Area

18489.40 1861.11

Perimeter

574.14 32.25

Horizontal

Diameter

159.24 6.30

Vertical

Diameter

162.39 6.38

Test 4

Area

19133.60 1497.48

Perimeter

698.84 68.99

Horizontal

Diameter

163.64 6.70

Vertical

Diameter

163.32 6.27

Test 5

Area

16065.20 1281.32

Perimeter

529.56 47.55

Horizontal

Diameter

153.01 3.09

Vertical

Diameter

150.80 4.58808

Test 6

Area

16112.10 2789.19

Perimeter

571.28 86.97

Horizontal

Diameter

147.74 11.03

Vertical

Diameter

153.64 12.29

Test 7

Area

33239.40 1991.74

Perimeter

1168.96 267.96

Horizontal

Diameter

211.41 3.93

Vertical

Diameter

239.35 25.56

Test 8

Area

10985.10 1161.59

Perimeter

533.17 82.11

Horizontal

Diameter

128.34 4.06

Vertical

Diameter

125.25 5.35

Test 9

Area

29139.60 1974.06

Perimeter

1989.11 916.88

Horizontal

Diameter

197.40 4.75

Vertical

Diameter

248.56 14.49

Note 1: Subjects 7 and 8 used contact lenses on purpose so that

the influence of contact lenses in the measurement could be

studied.

Note 2: All results are in number of pixels, since the zoom lens

changes the size of each pixel.

An example of the output data for one subject is

shown below:

Figure 9: Example of the pupil detection of an amount of

16 consecutive frames. All frames look alike because this

measurement was made at high speed.

Eye color: Brown.

Number of images: 100.

DEVELOPING A PUPILLOMETER

317



Figure 10: Plot of the Area of each frame along all 100

images.



Figure 11: Plot of the Perimeter of each frame along all

100 images.

Figure 12: Plot of the Horizontal diameter of the pupil

along all the thirty images.

Figure 13: Plot of the Vertical diameter of the pupil along

all the 100 images.

Output .txt file

14:24

03-Oct-2008

Id: Test 1

Threshold: 3

Area Perim Dist_h Dist_v

12282.0 441.2 133 131

13289.5 458.2 134 131

12339.0 458.2 132 130

(…)

Elapsed processing time: 00:07:14 (hms).

5 RESULTS AND DISCUSSION

The acquired results displayed in table 1 show that

the subject’s pupil dilated during the period of

accommodation. It is clear that the pupil size

changed during the acquisition period and, since this

is a preliminary study, the results fit the objectives.

However there is some error caused by the reflex of

visible light in the images taken.

The pupils of subjects 7 and 9 showed more

dilatation, since they suffer from myopia. These

people tend to have a larger pupil, as this study

successfully shows. A careful analysis of the pupil’s

diameters plots (figures 12 and 13) show that the

pupil’s fluctuations behave like a sinusoidal curve.

This behaviour is very interesting, for we can predict

the period of the sinusoidal curve.

A detailed analysis of the plots shows that we

can relate the pupil’s fluctuations to the sympathetic

and parasympathetic nervous flows in the subject’s

brain. This feature can be used in the future to

compare the pupil’s fluctuations in a normal subject

those of the pupil of a subject suffering from a

neurological disorder (Alzheimer’s or Narcolepsy,

for example).

By analyzing the statistical values of the

processed data we understand that the area and

perimeter algorithms must be optimized, since they

are expressing an error of about 7% and 5% (Matlab

error analysis), respectively.

Because grey scale (256 levels) images were

used, the system has the ability of detecting smaller

variations when

by comparing to some other studies

made in this area, such as IACOVIELLO’s (2006).

This feature allows the detection of small variations

in the digital image, so that the error can be reduced

and more information gathered.

6 CONCLUSIONS

This paper presents a complete system that can

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

318

accurately measure the dynamics of pupillary

movements given various stimuli. This is commonly

considered one of the most important parameters to

make non-invasive diagnosis of many neurological

disorders.

This system has a very user friendly interface that

will allow doing clinical trials by people not

specialised in this specific technique.

The acquired results showed some error in the

contour detection. In order to surpass this problem

the detection algorithm must be optimized and a

different type of camera must be used. Since the tests

were made using a camera that works in the visible

wavelengths, there was some light reflex in the

subject’s eye. For better results an infrared camera

should have been used, but no such camera was

available at the time of this study.

The results also showed some pupil noise, that is

chiefly due to the sympathetic and parasympathetic

neural flow. The main algorithm has been improved

to a good ratio between statistical error and

processing speed.

It is relevant to say that the detection algorithm

does not use physical models and does not

approximate the pupil contour to any geometrical

figure. The speed of the algorithm is compromised

but the results are more precise.

This study sets out to design a medical

instrument that can be used by any technician or

physician to measure pupil dynamics. For example,

it could be attached to a hospital bed, to monitor the

pupil activity of patients. The interface is compiled

in a .exe file that can easily be installed in every

computer even if the computer does not have Matlab

installed.

6.1 Future Perspectives

To improve the existing system it is clear that it must

evolve into an optical device that works in infrared

light. With this feature the light reflexes in the eye

will not influence the acquired data and the system

will also work in the dark. Using a CCD camera and

an infrared light system, an image of the anterior

surface of the eye can be obtained, even when

external lighting is not present (without interference

from non-controlled stimuli).

We intend to process the acquired data using

Fourier and Wavelet analysis, to work in frequency

and time domains.

The main algorithm will adapt to every image in

such a way that the threshold value will be

calculated for each frame, so that the digital image

features may vary (brightness, contrast and gamma).

In the developed GUI all results are expressed in

pixels, but, to facilitate the physician’s work, an

algorithm must be created to convert pixels into

millimeters. However, it will not be easy, since the

system uses a zoom lens and so the pixel size is

dependent of the zoom setting.

We believe that, in the near future this

methodology can be of assistance to Ophthalmology

diagnosis by quantifying the sympathetic and

parasympathetic pupillary dilatation components.

ACKNOWLEDGEMENTS

We thank the Department of Physiology of the

Institute of Molecular Medicine for technical advice

and for sponsoring the project, and the colleagues of

Hangar 4 of FCT-UNL for laboratory assistance, and

the colleagues of the Institute of Biophysics and

Biomedical Engineering for technical advice and

helpful discussions.

REFERENCES

Enderle, John, Susan Blanchard and Joseph Bronzino.

Introduction to Biomedical Engeneering. 2

Edition.

San Diego: Academic Press, 2005.

Filipe, João A. Capão, F. Falcão Reis and J. Castro

Correia. “Assessement of autonomic function in high

level athletes by pupillometry”. Autonomic Neuro-

science: Basic and Clinical. Vol.104. 2003, pp. 66-72.

Gonzalez, Rafael C., Richard E. Woods and Steven

L.Eddins. Digital Image Processing using MATLAB.

New Jersey: Prentice Hall, 2004.

Hachol, A., et al. “Measurement of pupil reactivity using

fast pupilometry”. Physiol. Meas. Vol.28. 2007, pp. 61-72.

Howarth, Peter A., Gordon Heron and Louise Wittaker. “

The Measurement of Pupil cycling time”. Graete's

Arch Clin. Exp. Ophthalmol. Vol.238, 2000, pp. 826-832.

Iacoviello, Daniela. “Analysis of pupil fluctuations after a

light stimulus by image processing and neural

network”. Computers and Mathematics with

Applications. Vol.53. 2006.

Kardon, Randy. 2003. “Pupil”. In Adler's Physiology of

the Eye. Edited by Paul L. Kaufman and Albert Alm.

10th Edition. St Louis : Mosby, 713-743.

Kim, Jieun, Kyungmo Park and Gon Khan. 2004. “A

Method for Size Estimation of Amorphous Pupil in 3-

Dimensional Geometry”. Paper presented at the 26th

Annual International Conference of the IEEE EMBS,

in San Francisco.

Li, Dongheng and Derrik J. Parkhurst. “Starburst: A robust

algorithm for video-based eye tracking”. openEyes.

http://thirtysixthspan.com/openEyes/publications.html.

Rakshit, Soumyadip and Donald M. Monro. Pupil Shape

Description using Fourrier Series. 2007.

Smith, Shirley A. and S. E. Smith. “Pupil function: tests

and disorders”, in MATHIAS, Christopher J. and Sir

Roger Bannister, Autonomic Failiure: A Textbook of

Clinical Disorders of the Autonomic Nervous System.

Edition. Oxford : Oxford University Press, 2001.

DEVELOPING A PUPILLOMETER

319