Diabetic Retinopathy

1. 1
“DIABETIC RETINOPATHY”
A
seminar on
GUIDED BY:
GROUP NO.
PRESENTED BY :

2. 18-Nov-22
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CONTENTS
• Abstract
• Introduction
• Literature Survey
• Block Diagram and Description
• Hardware/ Software Design
• Probable Results
• Advantages/Disadvantages
• Applications
• References
• Phase Wise Plan
• Conclusions

3. AIM
Aim of our project is to detect the
DIABETICRETINOPATHY
using image processing.
3
A I M

4. To develop a system which identify patients with “Diabetic
Retinopathy"with the help of MRI images.
To develop an algorithm to process the fundus image
and detect the lesions caused by diabetic retinopathy.
To determine the actual position and dimensions of the
spots.
To determine the percentage of vision loss.
4
OBJECTIVES

5. TRACT
5
ABS
Diabetic retinopathy is an eye abnormality caused
by long term diabetes and it is the most common
cause of blindness. Microaneurysms resulting from
leakage from retinal blood vessels, are early
indicators of DR, yielding a large body of diagnostic
work focused on automatic detection of MA.
However, automated detection of diabetes is
difficult because the small size of MA lesions and
low contrast between the lesion and its retinal
background, the large variations in color,
brightness and contrast of fundus images, and the
high prevalence of false positives in regions with
similar intensity values such as blood vessels, noises
and non-homogenous background.

6. TRACT
6
ABS
In this system, we analyzed diabetes
detectability from retinal images in the
Diabetic Retinopathy Database Calibration Level
raw pixel intensities of extracted patches
served directly as inputs into the following
classifiers: CNN .

7. RODUCTION
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7
INT
Diabetic Retinopathy(DR) is a progressive
disease with almost no early symptoms
of vision impairment, which is the
leading cause of blindness prior to the
age of 50 .
The first detectable sign of DR is the
presence of microaneurysms (MAs),
which result from leakage of tiny blood
vessels in the retina and manifest
themselves as small red circular spots on
the surface of retinas.

8. RODUCTION
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INT

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MOT
Thermal imaging modality recently used
in breast cancer detection, diabetic foot
and various eye diseases such as dry eye,
glaucoma, Meibomian gland dysfunction
and thyroid eye diseases. Diabetic eye
disease is a chronic disease affects various
organs of human body including the eye.
Infrared thermography offers a digitized
thermal distribution called thermograph.
It indicate the observation are with a
thermal variation of the surface
temperature.

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PROBLEM DEFINATION
Diabetic Retinopathy involves the growth of
abnormal blood vessels in the retina.
Complications can lead to serious vision
problems: Vitreous hemorrhage.
The new blood vessels may bleed into the
clear, jellylike substance that fills the center
of your eye.

11. 11
PAPER NAME AUTHOR NAME DESCRIPTION
LITERATURE SURVEY
Segmentation
of Retinal
Blood Vessels
using
Artificial
Neural
Networks for
early detection
of Diabetic
Retinopathy
Dr. Kulwinder
S Mann1,a)
and
Sukhpreet Kau
There are various eye diseases in the patients
suffering from the diabetes which includes
Diabetic Retinopathy, Glaucoma,
Hypertension etc. These all are the most
common sight threatening eye diseases due to
the changes in the blood vessel structure. The
proposed method using supervised methods
concluded that the segmentation of the retinal
blood vessels can be performed accurately
using neural networks training. It uses
features which include; Moment Invariant
based features, Gabor filtering, Intensity
feature for feature vector computation. Then
the feature vector is calculated using only the
prominent features.

12. 12
PAPER NAME AUTHOR NAME DESCRIPTION
LITERATURE SURVEY
Visual Analysis
of Aneurysm
Data using
Statistical
Graphics
Monique
Meuschke,
Tobias Gunther
This paper presents a framework to explore
multi-field data of aneurysms occurring at
intracranial and cardiac arteries by using
statistical graphics. The rupture of an
aneurysm is often a fatal scenario, whereas
during treatment serious complications for
the patient can occur. Whether an aneurysm
ruptures or whether a treatment is
successful depends on the interaction of
different morphological such as wall
deformation and thickness, and
hemodynamic attributes like wall shear
stress and pressure. Therefore, medical
researchers are very interested in better
understanding these relationships

13. 13
PAPER NAME AUTHOR NAME DESCRIPTION
LITERATURE SURVEY
Detection of
Diabetic
Retinopathy
using Image
Processing and
Machine
Learning
Salman Sayed 1 ,
Dr. Vandana
Inamda
Diabetes is a chronic disease caused by either
failure of insulin production in body or
inability to resist insulin in the body.
Complications of Diabetes leads to heart
disorders, vascular disease and stroke, Kidney
disease, Neuropathy and Diabetic eye disease
known as “diabetic retinopathy”. Diabetic
retinopathy is classified into two categories,
non-proliferative diabetic retinopathy (NPDR)
and proliferative diabetic retinopathy (PDR).
In this paper, detection of diabetic retinopathy
in fundus image is done by image processing
and machine learning techniques. Probabilistic
Neural Network (PNN) and Support vector
machines (SVM) are the two models adopted.

14. 14
PAPER NAME AUTHOR NAME DESCRIPTION
LITERATURE SURVEY
AUTOMATED
BLOOD
VESSEL
SEGMENTAT
ION IN
RETINAL
IMAGE
Pradeepa.BMr.,
J.Benadict Raja
The First step in development of automated
system for ophthalmic diagnosis is
Automatic segmentation of blood vessels
from retinal images. The abnormal image,
cottonwoolspots and hard exudates makes
the vessel extraction process as difficult task.
A Support Vector Machine (SVM) based
supervised method is proposed for
extracting blood vessel in retinal fundus
image. In this work, the green channel
image is enhanced by Gabor filter, and then
various features are extracted by
Thresholding method for SVM classifier.
The method will be implemented on publicly
available digital retinal images for vessel
extraction (DRIVE) database.

15. 15
PAPER NAME AUTHOR NAME DESCRIPTION
LITERATURE SURVEY
Retinal blood
vessel
segmentation
employing image
processing and
data mining
techniques for
computerized
retinal image
analysis. Retinal
blood vessel
segmentation in
fundus images
GeethaRamani,
Lakshmi
Balasubramanian
Most of the retinal diseases namely
retinopathy, occlusion, can be identified
through changes exhibited in retinal
vasculature of fundus images. Thus,
segmentation of retinal blood vessels aids
in detecting the alterations and hence the
disease. Manual segmentation of vessels
requires expertise. Employing
computational approaches for this
purpose would help in efficient retinal
analysis. The resultant segmented image
is formed through combining the results
of clustering and ensemble classification
process

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NICAL SPECIFICATION OF PROJECT
TECH
HARDWARE REQUIREMENTS:
System : Intel I5 Processor
Hard Disk : 20 GB.
Monitor : 15 VGA Colour
Ram : 8 GB
SOFTWARE REQUIREMENTS:
Operating system : Windows 7 and above
Coding Language : Python
IDE : Spyder

17. JECT SCOPE
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PRO

18. NO TASK DURATION
(Days/Months)
START DATE END DATE
1 Group Formation
2 Decide Area Of Interest
3 Search Topic
4 Topic Selection
5 Sanction Topic
6 Search Related Information
7 Understanding Concept
8 Search Essential Document
(IEEE & White Paper, Software)
9 Problem Definition
10 Literature Survey
11 SRS
12 Project Planning
13 Modeling& design
14 Technical Specification
15 PPT
18-Nov-22
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PROJECT TIMELINE CHART

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ASSUMPTIONS
AND
DEPENDENCIES
USING PYTHON
LANGUAGE
INPUT AS
TEXTUAL DATA
1
2

20. ITECHTURE DIAGRAM
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ARCH

21. HEMATICAL MODEL
Let S be the Whole system S= {I,P,O}
I-input
P-procedure
O-output
Input( I)
I={ Image}
Where,
Image -> Image Dataset
Procedure (P),
P={I, Using I System perform operations and calculate
Diabetic retinopathy.}
Output(O)-
O={System detect the Diabetic Retinopathy }
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MAT

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ALGORITHM
CNNs are primarily used for image
classification and recognition. The
specialty of a CNN is its convolutional
ability. The potential for further uses of
CNNs is limitless and needs to be
explored and pushed to further
boundaries to discover all that can be
achieved by this complex machinery.
CONVENTIONAL NUERAL NETWORK

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DFD O

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DFD 1

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DFD 2

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UML
DIAGRAM

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CLASS
DIAGRAM

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SEQUENCE
DIAGRAM

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USECASE
DIAGRAM

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BLOCK
DIAGRAM

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8.5
User Friendly Helps to detect
diabetic retinopathy
ADVANTAGES , DISADVANTGES AND APPLICATIONS

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CLUSION
CON
Extracting blood vessels from retinal
images help in early diagnosis of eye
diseases. We are proposing automated
system for extracting blood vessels
from retinal images. System uses
advanced image processing techniques
including classification of image pixels
into vessels or non-vessels and
classification of retinal images into
normal or abnormal. This helps in
diagnosis of different types of eye
disease.

33. ERENCES
[1] Segio B. Junior and D. Welfer, “Automatic Detection of
Microaneurysms Ang Haemorrhages in Color Eye Fundus
Images,” Southeast Asian J Trop Med Public Heal., vol. 34, no.
4, pp. 751–757, 2003.
[2] S. S. Rahim, C. Jayne, V. Palade, and J. Shuttleworth,
“Automatic detection of microaneurysms in colour fundus
images for diabetic retinopathy screening,” Neural Comput.
Appl., vol. 27, no. 5, pp. 1149– 1164, 2016.
[3] S. S. Rahim, V. Palade, J. Shuttleworth, and C. Jayne,
“Automatic Screening and Classification of Diabetic
Retinopathy Fundus Images,” Commun. Comput. Inf. Sci., vol.
459 CCIS, no. Dm, pp. 113–122, 2014.
[4] B. Antal and A. Hajdu, “Improving microaneurysm detection in
color fundus images by using context-aware approaches,”
Comput. Med. Imaging Graph., vol. 37, no. 5–6, pp. 403–408,
2013.
[5] A. Sopharak, B. Uyyanonvara, and S. Barman, “Fine
Microaneurysm Detection from Non-dilated Diabetic
Retinopathy Retinal Images Using a Hybrid Approach,” vol. II,
pp. 6–9, 2012.
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REF

34. ERENCES
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REF
[6] K. M. Adal, D. Sidibé, S. Ali, E. Chaum, T. P. Karnowski,
and F. Mériaudeau, “Automated detection of
microaneurysms using scaleadapted blob analysis and semi-
supervised learning,” Comput. Methods Programs Biomed.,
vol. 114, no. 1, pp. 1–10, 2014.
[7] R. A. Welikala et al., “Automated detection of proliferative
diabetic retinopathy using a modified line operator and dual
classification,” Comput. Methods Programs Biomed., vol. 114,
no. 3, pp. 247–261, 2014.
[8] Sharad Kumar Yadav, Shailesh Kumar, Basant Kumar,
and R.Gupta “Comparative Analysis of Fundus Image
Enhancement in Detection of Diabetic Retinopathy.”
Humanitarian Technology Conference (R10- HTC), 2016 IEEE
Region 10, 21-23 Dec. 2016

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Sr.
No
Activity Date Remark
s
1. Registration of Project Groups
2.
Discussion of Project idea /Topics (At least
3)
3. Finalization of Project Guide
4. Reporting to Respective Project Guides
5.
Submission of Abstract and Synopsis to
project Guides and Project Coordinator in
Prescribed Format
6.
Literature Survey ( At least 5 IEEE or similar
Papaers)
7.
Presentation on Selected Topic in front of
committee members (Ist Presentation)
SE WISE PLAN
PHA

36. 36
THANK YOU!!!!