Undergraduate Project Email

Automation of Diabetic

Retinopathy Detection

Undergraduate projectby Tanvee Chheda

Retinal Image acquisition from Ophthalmologist

Result comparison with that of Ophthalmologist

Image Processing and Automation algorithm in

MATLAB

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PROJECT

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August 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




Image Processing and Automation algorithm in

MATLAB

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Stop

PROJECT

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Image

Processing and

Automation

algorithm

Flowchart

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Detection of Microaneurysms using Bottom Hat transform and Morphological operators

Identification of Blood Vessels using Bottom Hat transform

Identification of Optic Disk using Hough Transform

Retinal Image acquisition

Retinal Image plane separation

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Retinal Image Plane Separation

Red plane( Optic Disk

detection)

Green plane( Microaneurysms and Blood vessels detection)

Blue plane


Image

Processing and

Automation

algorithm

Flowchart

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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

Image

Processing and

Automation

algorithm

Flowchart

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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


Image

Processing and

Automation

algorithm

Flowchart

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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




Image processing and automation algorithm in

MATLAB

Start

Stop

PROJECT

FLOWCHART


MAs marked by Ophthalmologist MA s detected using MATLAB algorithm



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


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

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68 9 8

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FN TP

Input Retinal Image

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f M

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imag

e(T

P+

FN

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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 speedAugust 03,2010 28Confidential Information

THANK YOU


Undergraduate Project Email

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