Fundus Imaging Based Affordable Eye Care

Kolin Paul and Vijay Kumar

Amarnath Khosla School of Information Technology, IIT Delhi, New Delhi, India

Keywords:

Glaucoma, Ophthalmoscope, Fundus, Image Processing, Android, OpenCV, CDR Ratio.

Abstract:

India is a developing country. It has a very low doctor to patient ratio and hence affordabe healthcare has

always been a matter of concern. The high cost of diagnostic devices and unskilled or limited skilled clinicians

exacerbate this problem. The treatment of visually impaired patients is a daunting task in India as it requires

periodic examination of eyesight. Devices meant for this check up are expensive and often require high degree

of expertise to operate. In this paper we propose a low-cost, lightweight, handheld, android phone based

fundus imaging device. The application software makes it user-friendly and also can also provide a limited

automatic screening capability of some retinal problems. In this paper, we report the use of this device for

screening of patients for Glaucoma. The paper presents empirical results using patient data from hospitals.

The results indicate that such a device can effectively scale the operations of screening of many eye related

problems in under served areas.

1 INTRODUCTION

There are more than 285 million people worldwide

suffering from some form of visual impairment. Out

of them, 39 million are victims of preventable blind-

ness caused by cataracts (51%), Glaucoma (8%),

child blindness (5%), trachoma etc. According to

a recent WHO report 90% of these people live in

developing countries that lack specialized medical

services, sophisticated medical equipment and clin-

icians (BlindnessCause1; BlindnessCause2). Many

of these are cases of preventable blindness like glau-

coma. Currently, in India, it is estimated that there

are 12 million people suffer from glaucoma and the

majority of them are undiagnosed (Thomas). This

clearly suggests that many cases of of visual im-

pairment can be mitigated by early stage detection.

For this to be possible a periodic but comprehensive

examination of the eye is mandated. The diagnos-

tic equipment (Optical Coherence Tomography, Hei-

delberger Retinal Tomography, Fundus Camera, Slit

Lamp etc.) employed for eye examination are bulky,

expensive and a trained technician is required to han-

dle them. In under served areas, the large number of

patients and time constraints of available opticians or

ophthalmologists are signiﬁcant constraints and there

is a need for an innovative solution. To address these

problems we have developed a hand held low cost

fundus imaging device coupled to the smart phone de-

vice that could help in the early detection of the eye

Figure 1: Working of mobile based fundus imaging sensor.

related ailments.

In recent times the smartphone has emerged as a

versatile mobile compute platform. The relative ease

of using a Smartphone based medical devices for var-

ious physiological parameter monitoring can provide

cost effective solutions to the problems of portability.

A Smartphone can be interfaced with a low cost unit

for examining the interior surface of eye, termed as

Fundus which is located opposite to the eye lens and

includes the retina, optic disc, macula and fovea, and

posterior pole. It can be examined using an ophthal-

moscope. Image acquisition of the Fundus is the most

critical aspect of the design — ophthalmologists use

high resolution fundus imaging devices for the same.

The proposed Fundus imaging device integrates the

phone’s inbuilt camera with an ophthalmoscope. A

mobile case has been prototyped for coupling the oph-

634

Paul K. and Kumar V..

Fundus Imaging Based Affordable Eye Care.

DOI: 10.5220/0005285006340641

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 634-641

ISBN: 978-989-758-068-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 2: (a)System Architecture and (b) Application software Flow chart.

thalmoscope to the phone. Android based application

software enables the capture of fundus Image/Video.

The application enhances the quality of the captured

image/video and detects the presence of Glaucoma.

The rest of the paper has been organized are fol-

lows. We begin with a high level system overview,

which brieﬂy discusses the system architecture and

its functional sections i.e. image acquisition, storing,

processing and diseases determination. Next, we de-

scribe the hardware and application software of low

cost fundus imaging device. Finally we provide ex-

perimental results of using the device along with vali-

dation results of hospital trials, to illustrate the porta-

bility, scalability and adaptability of the proposed so-

lution.

2 DESIGN OVERVIEW

In this section we outline the design of a Fun-

dus Imaging device subject to constraints of Cost,

Bulkiness, Portability and adaptability. As men-

tioned earlier, the device is based on the Smart-

phone. The Device application software enables a

semi trained/untrained user to operate the system for

detecting certain eye ailments. This has been made

possible by embedding the Optical (image captur-

ing unit), Computing (Microprocessor), Commu-

nication media (Wi-Fi, Bluetooth, Web etc.) and

Database functionalities into a single device shown in

Figure 2a. As illustrated in the ﬁgure, the device con-

sists of different function blocks which are described

below:

• Optical System.

This block captures the magniﬁed fundus image

with a camera — typically CMOS in nature. The

key innovation in the solution is to replace the

eye of the ophthalmologist by the smart camera

based CMOS sensor to capture the image of the

patient’s fundus as shown in Figure 4. We use a

smart phone of high resolution (> 5MP) Camera

for the same purpose. We have also designed and

built a custom mobile phone holder Rapid Proto-

type model to attach the Mobile phone camera to

the ophthalmoscope.

• Image Acquisition and Data Management

The Smartphone CMOS camera captures the

magniﬁed fundus image formed by ophthalmo-

scope and saves the image/video in the phone

memory or SD Card. The data is organized in a

hierarchical method. The image is stored along

with meta data like Patient ID and left/right eye

information.

FundusImagingBasedAffordableEyeCare

635

• Image Processing

This functional block does all the image process-

ing related tasks and it is broadly divided into two

units, viz., Processing-I and Image Processing-II

is shown in Figure 2a. To do the image process-

ing, the device uses Android based application

software and the OpenCV library. The ﬁrst sec-

tion removes helps to minimize/reduce the noise

present in fundus image by applying appropri-

ate median and other ﬁlters. After preprocessing

tasks, the next block performs the tasks related to

detection of ocular ailments (like Glaucoma, Dia-

betic retinopathy etc).

• Central Processing and Control Unit

An ARM based processor is used for overall con-

trol of the device. It uses the smart phone’s inbuilt

ARM Cortex processor and runs the application

software written in Java. The ARM based proces-

sor is able to satisfy the computing speciﬁcations

required for image processing and responsiveness

of the application software. The ARM processor

that was used for above operations had the follow-

ing speciﬁcation:

– CPU clock: 0.8 GHz to 2 GHz

– Core: 1-4

– Instruction Set: ARMv7

• Result and Data Representation

This section is responsible for sharing the fundus

data and results with other device or remote user

via different communication media either locally

or using wireless methods.

The Fundus Imaging system consists of smart

phone and an ophthalmoscope. Speciﬁcally, a con-

sumer grade (Samsung Galaxy S3/S4 ) cellular phone

is interfaced to a PanOptic’s ophthalmoscope. The

photo-imaging unit of the phone consists of a ﬂash

light as the illumination source to a 8.0 Megapixel

(MP) camera at the centre to centre separation of

around 10 mm. The ophthalmoscope used provides

an easy entry into the eye, together with a wider ﬁeld

of view to better observe eye conditions.The unit cap-

tures the fundus image in a JPEG format, which gets

stored in the phone. The image is then processed for

detection of its optical disk and cup, after which the

respective areas are calculated to compute the Cup to

Disc Ration (CDR) for setting up the threshold for

the affected eyes. The image processing operations

as discussed above are built into the Android Appli-

cation. The setup for capturing the fundus image of

a person’s eye using the developed system is shown

in Figure 3. The system is held very close to the per-

son’s eye (for detection of Glaucoma) The real time

application on the phone displays the image. Also,

Figure 3: System Setup for taking Fundus Image,In which

fundus image is appear on mobile phone screen.

it displays the result in a format which can easily be

interpreted.

We now describe the image magniﬁer system

which is essentially an ophthalmoscope and then de-

scribe the acquisition system which is the smart phone

camera.

Optical System The ophthalmoscope is an optical in-

strument for examining the interior structure of the

eye, especially the back part of eye (fundus), which

includes the retina, optical disk, optical cup, blood

vessel and fovea etc. Ophthalmoscopes are of two

kinds, direct and indirect (Timberlake).

Figure 4: Optical System: Doctor eye is replaced by Cam-

era.

We use PanOptic ophthalmoscope which is very

similar to the traditional ophthalmoscope. However,

there are number of differences – in particular, the

view angle of the PanOptic is 4-5x wider i.e. 25

◦

Embedded System. For Image Capturing, Image

processing and Result Sharing we need an advanced

electronic system that has the capabilities described

in the previous section. Modern Smartphones have

many of the required compute capabilities in addition

to having only communication and data sharing capa-

bility. They fulﬁl the requirements of (a) High res-

olution CMOS camera (b) Centralized Control Sys-

tem to synchronise and control all processes (c) High

computation capability to perform image processing

HEALTHINF2015-InternationalConferenceonHealthInformatics

636

and (d) communication features for sharing result and

data with a potential remote user. The proposed so-

lution has been developed on an Android based Sam-

sung Galaxy S-3 smart phone. The Smart phone cam-

era is able to capture fundus image only when if it is

properly interfaced with hand held portable ophthal-

moscope. In the proposed solution, the physician’s

eye (Figure 4) is replaced by CMOS Camera. For that

we have designed a special type of a phone holder as

shown in Figure 5.

Figure 5: (a) RP model of Case and (b) Ophthalmoscope

attached with mobile phone holder.

The mobile phone case CAD model is shown in

Figure 5, has been designed using SolidWorks soft-

ware. The material used in Rapid Prototype system

for building the case is SLS. Various factors as porta-

bility, light weight, aesthetic looks etc. have been

considered. The round shape on the top centre has

been provided to ensure that the view through the oph-

thalmoscope can be captured on phone’s camera with-

out any obstructions. For a stable ﬁt of the ophthal-

moscope with phone, a holder is built at the bottom.

Also, to ensure easy charging of the phone, a rectan-

gular shape hole is provided at the bottom and one on

the top left for the volume control. The mobile case

when integrated with the Ophthalmoscope is shown

in Figure 5. The mobile can easily be inserted into

the casing.

3 IMAGE ACQUISITION

SOFTWARE

In order to display the fundus image and the results, a

fundus imaging software has been developed. The ap-

plication software works on all Android based Smart-

phones. The application software includes all the

image processing operations needed to calculate the

degree of Glaucoma in the person’s eye. The same

has been achieved using OpenCV Library on Android

platform and performs two major tasks namely (a)

Image Acquisition and (b) Image Processing (Glau-

coma Detection). For all the tasks, application soft-

ware switches to different UI screens thus helping

guide the user at each stage of the application. This

is shown in Figure 2b. As shown in the ﬁgure, the

Fundus Image Acquisition step is further divided into

four stages.

• Filling Patient Information. GUI has two text

ﬁelds Patient Name and Patient ID. For each

user, patient ID is a must and it must be unique.

Patient ID will help in searching and sorting of

patient data in future.

• Option to use this device as new and old patient.

For both case application will provide separate

GUI windows.

• Separate GUI is provided to select left and Right

eye one by one for taking the fundus image.

• Image capturing GUI provides image retake and

save options. After the capturing operation, the

image is visible on thev GUI so that user can ac-

cept the image quality or redo the entire operation.

All the above four stages of operation in Android

is deﬁned as four different Activities and uses four

different Layouts. Image acquisition is done in four

stages. All stages are deﬁned as Activities in Android

Development environment (ADT). That is described

by the AndroidManifest ﬁle and brieﬂy described be-

low:

• BeginActivity: Patient Information Form. This

manages patient related information. User should

enter patient name and Patient ID No in the GUI.

On the basis of patients ID, the application soft-

ware will manage all the related data.

• MidActivity: Select Eye. MidActivity provides

a Menu option to select the eye and patient type

for Fundus imaging. By clicking the button/radio

button, the user makes the choice. The button’s

operation is deﬁned in the Activity Layout ﬁle.

• LeftCamActivity/RightCamActivity. In this ac-

tivity application will perform Video/Image aqui-

sition task. In this activity, the application cap-

tures images/video of left and right eye. To cap-

ture Video, application will call native Camera in-

tent. To use intent native Camera again applica-

tion will perform check for all camera permission.

However, in case of old patients, software will au-

tomatically search and select the patient fundus

image.

The next section describes then Android based ap-

plication developed for Glaucoma screening.

4 DETECTING GLAUCOMA

Glaucoma is a neurological disorder that causes dam-

age to optical nerves occurs due to Intra ocular

FundusImagingBasedAffordableEyeCare

637

pressure (IOP). Glaucoma is evaluated by measur-

ing IOP,visual feild and optical disc shape (Cup to

Disc Ratio ‘CDR’ (Narasimhan; Yuji) and ISNT rule

(Harizman)). CDR is one of the main clinical indi-

cations of Glaucoma. In case of glaucomatic eye this

ratio will high as compared to healthy eye.

In the application, we implement the intensity

based segmentation method (Yuji; Wong) to segment

out the optical disk and the cup from the green chan-

nel of ROI. Using the elliptical area formula android

device application software will calculate the area of

the optical disk and cup (Narasimham). After that,

we use the ellipse area formula to ﬁnd out the area of

segmented region. Finally CDR value is calculated by

taking the ratio of Cup Area and Disc Area. On the

basis of calculating CDR value one can say about the

stage of glaucoma.

We ﬁrst discuss the method used to identify the

ROI. We have used OpenCV library to implement im-

age processing operations for the application whose

primary focus to detect the glaucoma in the Fundus

image. The working of this Glaucoma detection soft-

ware is illustrated in the ﬂow chart shown in Figure 6.

The Android application performs image processing

in two stages. In the ﬁrst stage, image processing soft-

ware performs all the tasks related to ROI separation,

preprocessing and image quality detection. And in the

second stage, the software does Glaucoma detection.

Different algorithms from of Image enhance-

ment (Peli; Paulus) are used to de-noise the captured

image as well as perform operations for detection of a

particular ocular ailment. This includes the following

steps:

1. ROI Extraction. The ROI extraction is shown in

Figure 6. The application software captured fun-

dus image in RGB mode. The extraction of the

optical disk and the cup is done from from the G

plane as it provides better contrast compared to

the other planes.

The centre of the disk will be obtained on the basis

of maximum intensity region in the G plane/Gray

image. The identiﬁed maximum intensity point is

considered as a reference point to ﬁnd out ROI.

Using that value we separate out ROI of pixel

size 300 ×300 pixels or 500 ×500 pixels from the

original image.

2. Preprocessing of Extracted ROI. To differenti-

ate between optical cup and disk, a good qual-

ity of images is desirable. Also, even if the im-

age quality is good but edges are not easily differ-

entiable due to the inappropriate intensity levels,

image improvement is required. To achieve this,

the application performs Histogram Equalization,

Normalization and Filtration of blurred image.

Figure 6: Flow chart for ROI separation and Glaucoma De-

tection.

3. Automatic Image Quality Assignment. Some

time users are not able to differentiate the quality

of images taken by device. We have used tech-

niques described in (Paulus; Giancardo) to check

the image quality.

Figure 7: ROI:Blood Vessel.

• Elliptical Local Vessel Density Measure-

ment. In OpenCV we use different edge func-

tions to ﬁnd out edges in ROI. On the basis of

density edges in ROI we conclude about the

quality of images. Normally if the image is

good than available edges density is high.

• Histogram Based. The histogram is deter-

mined for the Gray Image. The application then

uses a method described in (Paulus) to ﬁnd out

the quality of image. Finally the application

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638

software applies both methods to ﬁnd quality

of image.

The application software uses this CDR value to

screen out Glaucoma suspects and health person. In

the proposed solution, one can also capture fundus

video also that will help a clinician to analyse retina in

other ways. The entire process is done on an undilated

eye. The application performs the following tasks one

by one to do the screening:

• Segmentation of Optical Disk and Optical Cup.

Optical disk detection is carried out in the an-

droid application software using the OpenCV li-

brary image processing function. Implementation

is done in following steps:

– RGB Fundus image is converted into a Gray

image: Used OpenCV library function (Fig-

ure 8).

– Histogram equalization of the image pixel in-

tensity: (Figure 8). After equalization, im-

age intensity value lies between 0-255 and it is

helpful in next stage i.e. thresholding.

– Thresholding and Binarization: In the applica-

tion software we used 95% of the maximum in-

tensity as a threshold point for Disk segmenta-

tion. (Figure 8).

The optical cup detection is similar to detection of

optical disk, the only difference being the selec-

tion of the threshold value (here it is 80% of the

maximum intensity gray image). The segmented

Cup is shown in Figure 9.

• Cup to Disk Ratio(CDR). Optical cup size will

increase as compared to optical disk and that will

reﬂected through Cup to Disk ratio. For a nor-

mal person its value is 0.1-0.3. CDR value plays

key role in our proposed solution for Glaucoma

screening. But in the case of the eye affected with

the disease, the ratio becomes high.

• Segmented Disk/Cup Area To ﬁnd CDR, ﬁrstly

ﬁnd out the area of Disk and Cup.Used the bina-

rized optical disc and cup to ﬁnd out correspond-

ing area using the ellipse method (Narasimhan;

Narasimham).

Area = π ∗a ∗ b (1)

Where,

a = half length of major axis ellipse.

b = half length of minor axis of ellipse.

After obtaining the respective areas, the CDR ra-

tio is calculated.

Figure 8: Segmentation: 1. Gray ROI 2. Historarm equal-

ized ROI 3. Segmented optical disc and 4. Segmented opti-

cal cup1.

Figure 9: Elliptical Shape of Segmented disk/cup.

5 EXPERIMENTATION AND

VALIDATION

This section discusses the experimentation conducted

at an Eye Hospital in two stages. In the ﬁrst stage we

took fundus image of 15 patients to ﬁnd out a thresh-

old CDR for glaucoma detection. In the second stage

we haved validate the device with more subjects. For

the whole process of experimentation, we have cho-

sen patients randomly and maintain similar test con-

ditions which are enumerated below:

1. Hospital Doctor will ﬁrst test patient eye for Glau-

coma using Tonometry and Ophthalmoscopy

2. Room Light: Dim room light is used for collecting

fundus image.

3. Patient Condition:

(a) Sitting on a chair in rest position

(b) For Un-dilated eye: Take a fundus image at the

same instant.

size is not sufﬁciently large. In that case doctor

are not able to see larger view of fundus image.

We dilate the eye, so that pupil Diameter will

increase and it will take about 20-30 minutes.

FundusImagingBasedAffordableEyeCare

639

(d) Cataract patient: If a patient is suffering from

cataract than normal fundus imaging is not able

to ﬁnd clear image of the retina. In that case

this device will be better because of image En-

hancement capability.

Figure 10: (a) Taking the fundus image colligue in labora-

tory and (b) Fundus image (Cpature by our device from the

hospital).

Repeatability Test

Initially a test was carried out on a healthy participant

in laboratory to verify the repeatability of the device.

In this case we will check segmented cup and disk on

Matlab. Then ﬁnd its Area and CDR. We got a stable

Table 1: Repeatability Test: Cup and Disc Area for a

healthy person.

S.No. Cup Area Disc Area CDR

1 26829 107980 0.2485

2 23320 107760 0.2165

3 23637 105160 0.2248

4 24014 117780 0.2039

5 28953 118930 0.2434

6 25447 903430 0.2817

7 19195 102310 0.1876

output for the same person repeatedly. Repeatability

test data are shown in Table. 1. From Table 1, mean

and standard deviation obtained from healthy person

is 0.2295 and +/-0.0313 respectively.

Threshold CDR Value for Glaucoma

Screening

After verifying stability of the device, several tests

were performed on participants at the Eye Hospital. It

was observed that the CDR value obtained for the pa-

tients with Glaucoma was always greater than 0.2809.

Whereas, the CDR value obtained for Non Glau-

coma patients was always less than 0.2809 (Table 2

and Figure 11). Hence, a threshold of 0.2809 was

set for screening Glaucoma patients from Non Glau-

coma patients. Also, the mean and standard deviation

obtained for Glaucoma and Non Glaucoma patients

Table 2: CDR values of glaucomatous and health person.

S.No Glaucoma patient Non glaucoma

(CDR) patient(CDR)

1 0.4266 0.2564

2 0.2809 0.2802

3 0.3799 0.2006

4 0.4761 0.2672

5 0.5599 0.1166

6 0.3243 0.2591

Figure 11: Graph: CDR value Curve.

were 0.4080, +/-0.1020 and 0.2300, 0.0619 respec-

tively.

Device Validation

Second stage of device experimentation, device vali-

dation is done in the same eye Hospital with 10 addi-

tional subjects. CDR values for these patient is shown

in Table 3. CDR value 0.2700 is considered as thresh-

old value for this device . In Table 3 result of patient

No 08 is false positive. As for screening device, this

is safer side because in the next stage of retinal diag-

nosis of patient, the problems was identiﬁed.

Table 3: Patient diagnosis report using mobile based fundus

imaging device, Here threshold value used for diagonosis is

0.2700.

S.No CDR LE CDR RE Result LE Result RE

1 0.302 +ve

2 0.335 0.522 +ve +ve

3 0.464 0.308 +ve +ve

4 0.0.043 0.168 -ve -ve

5 0.369 +ve

6 0.15 0.195 -ve -ve

7 0.381 0.261 +ve -ve

8 0.263 0.429 -ve False +ve

9 0.205 0.245 -ve -ve

6 CONCLUSION

The mobile phone based low cost, portable fundus

imaging device has been designed and developed to

HEALTHINF2015-InternationalConferenceonHealthInformatics

640

improve accessibility to affordable eyecare. It has

been shown that applications can be built to automati-

cally screen eye related ailments. the paper illustrates

results for screening patients with glaucoma — the

application is being enhanced for detecting other eye

ailments notably diabetic retinopathy. Primary exper-

iments and validation also proves its usability, and

easy handling capability. It has the capability to sup-

port different media of communication to share im-

age/result to remote user. It can be used in primary

health care center, OPD and Health care camp where

fast screening is necessary.

ACKNOWLEDGEMENT

This work was partially supported by Telecommuni-

cation Consultants India Limited (TCIL), DeITY and

IIT Delhi.

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