undergraduate project email

Automation of Diabetic

Retinopathy Detection

Undergraduate project

by Tanvee Chheda

Retinal Image

acquisition from

Ophthalmologist

Result comparison with

that of Ophthalmologist

Image Processing and

Automation algorithm in

MATLAB

Start

StopAugust 03,2010 2Confidential Information

What is Diabetic Retinopathy(DR)? It hampers normal vision ability by causing the

person to see black patches

High blood sugar level damages retinal vessels

causing DR

Silent disease in initial stage

Stages of DR Microaneurysms(MA)

Exudates

Hard exudates

Soft exudates

HemorrhagesAugust 03,2010 3Confidential Information

Microaneurysms

First unequivocal signs of DR

Tiny dilations of capillaries

Appear as reddish brown spots in

retinal fundus images

They increase in number as the

degree of retinal involvement

progresses

Play a key role in mass-screening and monitoring of DR

August 03,2010 4Confidential Information

Retinal Image

acquisition from

Ophthalmologist



Image Processing and

Automation algorithm in

MATLAB

Start


Stop

Detection of Microaneurysms

using Bottom Hat transform

and Morphological operators

Identification of Blood Vessels


Identification of Optic Disk

using Hough Transform

Retinal Image acquisition

Retinal Image plane separation

Start


Retinal Image Plane Separation

Red plane

( Optic Disk

detection)

Green plane

( Microaneurysms

and Blood

vessels detection)

Blue plane


Stop










Start


Features of Optic Disk(OD)

Appears in color fundus images as a bright yellowish or white region

Its shape is more or less circular, interrupted by outgoing vessels

Its size varies between different patients and is approximately 50 pixels in 576 x 768 color photographs


OD Detection Using Circular Hough Transform

Performed in red plane

Edge detection

Finds circles from an edge detected image

Suitable radius is given as input (30 ≤ R ≤ 40)

Falsely detected circular edges are eliminated by selecting the

largest circle

Original image OD marked imageAugust 03,2010 10Confidential Information

Stop










Start


Blood Vessels(BV ) Detection Acts as a landmark in grading disease severity

Prominent in green plane

Bottom Hat Transform

Enhances details for a gray scale image

It is given by the formula:

Bottom Hat image = (original image) – (closing image)


Original Image Closing Image

Bottom Hat

transformed Image


Canny edge detector

Uses two thresholds to detect

strong and weak edges

Noise resistant & highlights true

weak edges

BV Detection Cont….

Canny Edge Detected Image

Thresholded Image

Thresholding

It is used to highlight the

longer & connected

vessels, thus eliminating

smaller thread like structures


Original Image BV Marked Image


Stop










Start


Microaneurysms(MA) Detection Appear more contrasted in green plane

Candidate region possibly corresponding to MAs selected

Canny Edge Detected Image BV Thresholded Image

Subtracted ImageAugust 03,2010 17Confidential Information

Suitable threshold window

selected for exact detection of

MA on subtracted image

Morphological operators used to

find exact MAs on

threshold image

MA Detection Cont…

Threshold Image

Detected MAs August 03,2010 18Confidential Information

Original Image MAs marked image


MATLAB algorithm

Result


sing

All Detected Features


Retinal Image

acquisition from

Ophthalmologist



Image processing and

automation algorithm in

MATLAB

Start


MAs marked by Ophthalmologist MA s detected using MATLAB

algorithm

Result comparison with that of Ophthalmologist


MATLAB algorithm Performance

Characteristics Performance of the system is measured on the based on

Sensitivity and Accuracy

Sensitivity can be computed by the following equation:

Sensitivity=TP/(TP + FN)

Accuracy can be computed by the following equation:

Accuracy=FP/(FP+FN)

where,

TP is number of positives outcomes i.e. MAs accurately detected

FN is the abnormal sample classified as normal i.e. features which are

MAs but not detected by the algorithm

FP is the number of negative outcomes i.e. features that are not MAs

but detected as MAs


17

68 9 8

19 20

12

22

2 13 2

8 8

18

7

2

51

0

1

5

2

3

11

11

02

3

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

To

tal n

o. o

f M

As

per

ima

ge(T

P+

FN)

Input Retinal Image

FN

TP

Detection of MAs Manually Using MATALB

algorithm

Sensitivity 78.05% 82.10%

Sensitivity

Graph

Sensitivity Table


Accuracy Graph

Accuracy of the MATLAB algorithm is found to be 80%


Application

Facilitates management of Diabetic Retinopathy

The system can potentially reduce the number of retinal images

that the clinician needs to review by 60%

For preliminary detection of Diabetic Retinopathy by a general

physician, especially in rural areas where Opthalmologists are not

easily available


Future Scope Along with MAs which are the primitive signs of DR, long

persisting features like Hemorrhages and newly formed Blood

Vessels can be detected

Accuracy of the system can be further improved by considering

broader and more enhanced specifications of the features like

their inclination and connectivity to the neighboring blood vessels

Advanced types of DR like PDR, moderate NPDR and Diabetic

Maculopathy can be diagnosed on similar lines

A high speed processor or better computer programming

languages like visual C++, java etc can be used to improve the

processing speed


THANK YOU


undergraduate project email

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