undergraduate project email
DESCRIPTION
Teamed with 2 students to research and implement the automation of diagnosis of Diabetic Retinopathy and co-ordinated with an Ophthalmologist to verify our implementation. Responsibilities included MATLAB coding, algorithm testing, and product documentation. • Automation in MATLAB involving retinal image analysis to help Ophthalmologist increase the productivity and efficiency in a clinical environment.• Used Image Processing concepts such as Hough Transform, Bottom Hat Transform, Edge Detection Technique and Morphological Operators. Provided our algorithm and documentation to our research faculty advisor to enable him to continue this research to the next phase.TRANSCRIPT
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Automation of Diabetic
Retinopathy Detection
Undergraduate project
by Tanvee Chheda
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Retinal Image
acquisition from
Ophthalmologist
Result comparison with
that of Ophthalmologist
Image Processing and
Automation algorithm in
MATLAB
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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
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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
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Retinal Image
acquisition from
Ophthalmologist
Result comparison with
that of Ophthalmologist
Image Processing and
Automation algorithm in
MATLAB
Start
StopAugust 03,2010 5Confidential Information
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Stop
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
Start
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Retinal Image Plane Separation
Red plane
( Optic Disk
detection)
Green plane
( Microaneurysms
and Blood
vessels detection)
Blue plane
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Stop
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
Start
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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
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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
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Stop
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
Start
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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)
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Original Image Closing Image
Bottom Hat
transformed Image
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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
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Original Image BV Marked Image
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Stop
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
Start
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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
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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
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Original Image MAs marked image
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MATLAB algorithm
Result
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sing
All Detected Features
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Retinal Image
acquisition from
Ophthalmologist
Result comparison with
that of Ophthalmologist
Image processing and
automation algorithm in
MATLAB
Start
StopAugust 03,2010 22Confidential Information
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MAs marked by Ophthalmologist MA s detected using MATLAB
algorithm
Result comparison with that of Ophthalmologist
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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
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To
tal n
o. o
f M
As
per
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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
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Accuracy Graph
Accuracy of the MATLAB algorithm is found to be 80%
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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
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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
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THANK YOU
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