Automated Detection of Glaucoma using

K Means clustering algorithm

Project Report

Submitted in partial fulfillment of the requirements

For the degree of

Bachelor of Engineering

(Information Technology)

Name Roll No

Asmita Sarkar

2011-3027

Debangana Dutta

2011-3023

Snehangshu Dan

2011-3052

Debpratim Chakraborty

2011-3022

B.E 4th Year Dept:IT Batch:2011-2015

Under the Guidance of Mr.Arindam Chowdhury

UNIVERSITY INSTITUTE OF TECHNOLOGY

THE UNIVERSITY OF BURDWAN

BURDWAN

ACKNOWLEDGEMENT

A project is the result of hours of painstaking work and this is no

exception. There are people who deserve special mention here; without

whom this project would not have been possible.

First and foremost, we would like to thank Asst Prof. Arindam

Chowdhury, for his advice, supervision and crucial contribution, which

have made him the backbone of this project. We would also like to

thank our Head of the Department, Asst Prof. SOUBHIK

BHATTACHARYA for his invaluable advice; and the entire faculty of

the CSE-IT department of University Institute of Technology,Burdwan

for their cooperation and valuable input.

Finally, during a project, there are times when we lose the will to go on,

and wish the project were over then and there. Our friends helped us

endure those times with their unfailing humor and unflinching

encouragements. If we owe more to someone than others, they would

definitely be my parents, for their constant support and blessings,

without whom I would not be where I am today.

Certificate from the Project Guide

This is to certify that this project entitled “Automated Detection of Glaucoma

using K Means clustering algorithm” submitted in partial fulfillment of the

degree of B.E.,UNIVERSITY INSTITUTE OF

TECHNOLOGY,BURDWAN UNIVERSITY is done by-

Name Roll No Asmita Sarkar 2011-3027

Debangana Dutta 2011-3023

Snehangshu Dan 2011-3052

Debpratim Chakraborty 2011-3022

-is an authentic work carried out by them under my

guidance. The matter embodied in this project work has not been submitted

earlier for award of any degree to the best of my knowledge and belief, and the

project report has been developed according to the “B.E. PROJECT &

PROJECT REPORT STANDARD, THE UNIVERSITY OF BURDWAN”.

Signature of the Student Signature of the Guide

Self-Certificate

This is to certify that the dissertation/project report entited “Automated

detection of glaucoma using K Means Clustering algorithm”,

done by us, is an authentic work carried out for the partial fulfillment of the

requirements for the award of the degree of B.E, under the guidance of

Asst. Prof Mr.Arindam Chowdhury.

We also certify that We are aware of the “B.E PROJECT & PROJECT

REPORT STANDARD, THE UNIVERSITY OF BURDWAN” issued by the

University of Burdwan, and this project report is based on that standard. The

matter embodied in this project work has not been submitted earlier for the

award of any degree or diploma to the best of our knowledge and belief.

Signature of the

Students

Asmita Sarkar

Debangana Dutta

Snehangshu Dan

Debpratim Chakraborty

INDEX

Sl No

Topics Page

1) 2)

3)

4)

5) 6) 7) 8) 9) 10)

Objective Introduction a)What is glaucoma? b)Risk factors of glaucoma c)How can glaucoma be detected? d)What is optic disc & optic cup? e)What is K Means clustering? Methodology used in this project a)Flowchart of Methodology used. b)RGB Colour extraction c)G Plane extraction d)Region of Interest(ROI) extraction e)Cluster Identification using K Means algorithm f)Ellipse Fitting g)Cup to Disc Ratio Calculation(CDR) Examples of testing of the proposed methodology on a known normal and glaucomateous image a)Normal Image b)Glaucomateous Image Results Comparitive Study Methods Approached Previously Conclusion Future Scope References

1

2 3-4 5-6 7 8-9

11 12 12 13 13-15 15 16

17-18 19-20 21 22-28 29 30 31 32

1

Objective

To detect glaucoma in an optic image by

calculating the optic cup to optic disc ratio by

segmenting the optic image using k means

clustering.

2

Introduction

What is Glaucoma?

Glaucoma is a complicated disease in which

damage to the optic nerve leads to progressive,

irreversible vision loss. Glaucoma is the second

leading cause of blindness. Glaucoma has been

called the "silent thief of sight" because the loss

of vision often occurs gradually over a long period

of time, and symptoms only occur when the

disease is quite advanced. Once lost, vision cannot

normally be recovered, so treatment is aimed at

preventing further loss. Worldwide, glaucoma is

the second-leading cause of blindness after

cataracts. It is also the leading cause of blindness

among African Americans. Glaucoma affects one

in 200 people aged 50 and younger, and one in 10

over the age of 80. If the condition is detected

early enough, it is possible to arrest the

development or slow the progression with

medical and surgical means.

3

Risk Factors of Glaucoma:

Because chronic forms of glaucoma can

destroy vision before any signs or symptoms

are apparent, be aware of these factors:

Elevated internal eye pressure

(intraocular pressure).- If your internal eye

pressure (intraocular pressure) is higher than

normal, you're at increased risk of developing

glaucoma, though not everyone with elevated

intraocular pressure develops the disease.

Age- You're at a higher risk of glaucoma if

you're older than age 60, particularly if you're

Mexican-American. You may be at higher risk

of angle-closure glaucoma if you're older than

age 40

4

Medical conditions- Several conditions

may increase your risk of developing

glaucoma, including diabetes, heart diseases,

high blood pressure and hypothyroidism.

Other eye conditions-Severe eye injuries

can cause increased eye pressure. Other eye

conditions that could cause increased risk of

glaucoma include eye tumours, retinal

detachment, eye inflammation and lens

dislocation. Certain types of eye surgery also

may trigger glaucoma. Also, being near

sighted or farsighted may increase your risk of

developing glaucoma.

Long-term corticosteroid use-Using

corticosteroid medications, especially eye

drops for a long period of time may increase

your risk of developing secondary glaucoma

5

How can glaucoma be detected?

1.Elevated Intraocular pressure (>22 mmHg),

2. Increased cup-to-disc ratio(The cup-to-disc ratio

compares the diameter of the "cup" portion of the

optic disc with the total diameter of the optic disc.

A good analogy to better understand the cup-to-

disc ratio is the ratio of a donut hole to a donut.

The hole represents the cup and the surrounding

area the disc. If the cup fills 1/10 of the disc, the

ratio will be 0.1. If it fills 7/10 of the disc, the ratio

is 0.7. The normal cup-to-disc ratio is 0.3. A large

cup-to-disc ratio may imply glaucoma or other

pathology), and

3.The ISNT rule – It states that in a healthy optic

disc, the widest rim tissue is found inferiorly, then

superiorly, nasally, with the temporal rim being

the thinnest. This gives rise to a cup shape that is

often a horizontal oval.

6

We will use cup to disc diameter ratio for

detecting the possibility of glaucoma in the image

of an eye.

If the cup to disc ratio is greater than 0.3,the

patient is doubted to be suffering from glaucoma

and may be referred for further treatments.

7

What is optic disc and cup?

The optic disc is the anatomical location of the

eye's “blind spot", the area where the optic nerve

and blood vessels enter the retina . The optic disc

can be flat or it can have a certain amount of

normal cupping. Glaucoma produces additional

pathological cupping of the optic disc.

Fig-illustration of optic disc and

optic cup in an optic image

8

What is K Means Clustering?

K-means (MacQueen, 1967) is one of the simplest

unsupervised learning algorithms that solve the

well known clustering problem. The procedure

follows a simple and easy way to classify a given

data set through a certain number of clusters

(assume k clusters) fixed a priori. The main idea is

to define k centroids, one for each cluster. These

centroids shoud be placed in a cunning way

because of different location causes different

result. So, the better choice is to place them as

much as possible far away from each other. The

next step is to take each point belonging to a

given data set and associate it to the nearest

centroid. When no point is pending, the first step

is completed and an early groupage is done. At

this point we need to re-calculate k new centroids

as barycenters of the clusters resulting from the

previous step. After we have these k new

centroids, a new binding has to be done between

the same data set points and the nearest new

centroid. A loop has been generated. As a result

9

of this loop we may notice that the k centroids

change their location step by step until no more

changes are done. In other words centroids do

not move any more.

Finally, this algorithm aims at minimizing an

objective function, in this case a squared error

function. The objective function

,where is a chosen distance measure

between a data point xi(j) and the cluster centre cj,

is an indicator of the distance of the n data points

from their respective cluster centres.

10

Methodology used in this project-

This work uses K means clustering algorithm to

segment optic disc and optic cup from an optic

image and measure the optic cup and optic disc

ratios.

The work consists of mainly three stages of

operation as given below-

1.G plane extraction from the optic image

2.ROI(Region of Interest) extraction

3.Measurement of optic cup to optic disc ratio by

calculating their respective areas using ellipse

fitting.

All the stages are explained below in details.

11

Flowchart of the methodology used-

This work is based on the following flowchart

The important steps used in the flowchart are

explained in the next pages

12

1.RGB Colour Extraction

The first step is to take the input image and

separate the colour components of the grayscale

image into it’s red,blue and green components.

It is necessary for extracting the G component

only which helps in further processing of the

image for ROI extraction.

2.G Plane extraction

G plane is considered for the extraction of optic

disc and optic cup, because G plane provides

better contrast than the other two planes Red and

Blue planes. Hence it is necessary to separate the

G plane for further analysis.

13

3. Region of Interest (ROI Extraction)

After the extraction of the G Plane, we have to

take the region of interest for this project (which

is the optic disc and the optic cup).Thus, we have

to eliminate the outer regions of the eye and take

only the desired portion to be used for optic cup

to disc ratio.

4. Cluster identification using K Means algorithm

K-means clustering plays a vital role in the feature

extraction stage to compute the features (CDR). It

is an unsupervised learning algorithm that solves

the well-known clustering problem. The

procedure follows a simple and easy way to

classify a given data set through a certain number

of clusters (k clusters) fixed a priori.

This work implements a k-means algorithm that

finds the centres of n clusters and groups the

input samples around theclustersto define k

centroids, one for each cluster. At this point k new

centroids are calculated as the mean of

14

the clusters resulting from the previous step.

As a result of repetitive application of these

two steps, the k centroids change their

location step by step until no more changes

take place.

The K-means algorithm is an iterative

technique that is used to partition the ROI

image into K clusters. The ROI covers mainly

the entire optic disc, optic cup and a small

portion of other regions of the image. Hence

the K value is chosen as 3.

From the three clusters as shown in the optic

disc cluster has to be identified as follows. The

cluster which contains the border region

belongs to the other region of the ROI image

that does not contain the optic disc and optic

cup. Hence, the cluster is removed for the

extraction of optic disc and the remaining two

clusters form the optic disk region. Since it is

clearlyknown that the optic cup is inside the

optic disc,

15

The cluster in the center of the image forms the

optic cup .

After extracting optic disc, contours are found and

the contour with maximum area will correspond

to the optic disc.

5. Ellipse fitting

Rectangles are drawn for each optic disc and cup

areas enclosing their regions.then an ellipse is

drawn for each of the cup and disc areas

,inscribed within the rectangle.

The area of the ellipse as well as the area of the

optic disc is calculated by using the formula A =

πab where a and b are major axis length (half of

the rectangle width) and minor axis length (half of

the rectangle height), respectively. The area of

optic cup is also computed in the same manner.

16

6.Cup to Disc ratio calculation (CDR).

The CDR which is the ratio between the area

of the optic cup and the area of the optic disc

is computed and used as the feature for the

detection of glaucoma.

Cup to disc ratio=Area of optic cup/Area of optic disc

If the cup to disc ratio comes above 0.3, the

patient can be suspected to have glaucoma. If

the ratio comes below 0.3, the patient maybe

considered safe.

17

Examples of Testing of the proposed

methodology on a normal and a glaucoma image

(known)

1.Normal image

A normal image is taken from a standard

dataset and testing is done as follows.

Normal eye

image

G plane extraction

18

Region of

interest

K

clustered

Area Calculation by ellipse

fitting

Next, the CDR is calculated, which comes to

be 0.27 for this image thus confirming with

the dataset that it is a normal, non

glaucomateous image.

19

2.Glaucomateous image

A suspected glaucoma image is taken from a

standard database and the steps of the project

are performed as follows-

Glaucomateous image

G plane of the glaucomateous image

20

ROI Extraction of the glaucomateous

image

K means clustered glaucoma image

Area by curve fitting of the

glaucomateous image

Next,the CDR is calculated which comes to be 0.4,

thus confirming with the dataset that it may be

glaucomateous.

21

Results-

The proposed system is tested upon a batch of

455 images taken from a standard dataset of

normal and glaucomateous images.

Regions were correctly identified in 374 images.

The region detection failure rate is 18%.

Accuracy of the system is 60%(out of 374 images)

Accuracy

Total

455

81 images.

225

correct detection

149

incorrect detection

could not detect

22

Comparitive Study-

Two previous papers on optic disc and cup

segmentation using k means clustering have been

consulted for this project-

1.An efficient automated sysyem for glaucoma

detection using fundus image

By K Narasimhan, Dr. K Vijayrekha

2. Clustering Based Optic Disc and Optic Cup

Segmentation for Glaucoma Detection

V.Mahalakshmi ,S.Karthikeyan

23

The flowcharts of the above two papers

respectively are-

Flowchart of first paper

Flowchart of second paper

24

As can be noticed, there is not much difference in

the methods of their process and this project,but

some of the techniques used were different in the

three methods. Also, the accuracy of the three

methods are different due to the different

methods used.Thecomparision can be explained

as follows-

With Respect to First paper by By

K.NARASIMHAN, Dr.K.VIJAYAREKHA-

1.For the k means clustering their paper uses

three classifiers for the clusterization- KNN, SVM

and Bayes classifier are used to analyze the

performance of the proposed system.

Our System uses only BAYES Classifier for this. In

Bayes classifier, normal Gaussian distribution is

used to fit or model the feature base and it

estimates the prior probabilities from the relative

frequencies of the classes in training.

25

2. They also added features of ISNT Ratio in their

methodology

Results-

We have compiled our project on a standard

database for testing our proposed system which

consisted of 455 images.

The accuracy rate achieved in our project is nearly

60%

They have used A batch of 36 retinal images

obtained from the Aravind Eye Hospital,

Madurai, Tamilnadu, India to assess the

performance of the proposed system.

Using BAYES Classification they obtained 66%

accuracy,

26

With Respect to Second Paper byV.Mahalakshmi ,

S.Karthikeyan

The Main points which can be compared are-

1,They used Gabor Filter to reduce noise and edge

detection.It was necessary because they took

their dataset using a digital camera which may

have inherited a lot of noise . But our process has

a dataset which was very low in noise. Using

Gabor Filter may have increased complexity.

2.They have used vertical diameters for

calculating cup to disc ratios whereas we have

used ellipse fitting for area calculation and

calculated the ratio of the areas of optic disc and

cup.

3. They didn’t use G plane extraction which we

used, being a project in java

Results-

They have used only 8 normal and 8 abnormal

images.

27

Images are collected from Mahatma Eye care

Hospital,Trichirapalli . The images were acquired

using a Canon CR5 non–mydriatic 3 CCD camera

with a 45 degree field of view(FOV).Each image

was captured using 8 bits per colour plane at 768

by 584 pixels.The FOV of image is circular with a

diameter of approximately 540 pixels. For this

database, the images have been cropped around

FOV.For each image, a mask image is provided

that delineates the FOV.

They had nearly 61% accuracy using their method.

We have used nearly 455 images and used the

standard database from the net.

We have an accuracy of nearly 60 %

28

Conclusion from the comparision-

This work confirms nearly to the accuracies of the

previous works done with this K Means

clustereing method and thus can be considered

safe for testing optic images for the detection of

glaucoma.

29

Methods approached previously

Before K Means clustering, level set segmentation

was approached to calculate CDR.

But, level set segmentation was very complex to

implement as it is hugely based on partial

differential equations which could not be

programmed well enough.

K Means algorithm is easier to code and huge

number of medical images can be tested in this

algorithm in a short span of time reducing time

complexity which was harder in level set

segmentation.

30

CONCLUSION-

This paper presents K Means Clustering based

optic disc and cup segmentations for glaucoma

detection. Structural features like CDR (Cup to

Disc Ratio) are measured and if the ratio value

exceeds 0.3 shall be recommended for further

analysis of a patient This shall help in patients

worldwide by protecting further vision

deterioration through timely medical

intervention. We can increase the number of

patients and analyze the performance.

31

Future Scope-

1. ISNT Method can be used additionally with

this work to increase accuracy of the work.

2. K medians algorithm can be approached but

the boundaries in k medians approach may

not be clear

3. Many other modifications of this project can

be made based on situation.

32

References-

1.Digital Image Processing by Gonzalez

2.Wikipedia

3. An efficient automated system for glaucoma

detection using fundus image

K. Narasimhan,Dr. K Vijayarekha

Asstt Prof., Department of ECE, SASTRA

Assoc. Dean., Department of EEE, SASTRA

4. Clustering Based Optic Disc and Optic Cup

Segmentation for Glaucoma Detection

V.Mahalakshmi ,S.Karthikeyan

PG Student, Department of Electronics and

Communication Engineering, K. S. R. College of

Engineering, Tiruchengode, IndiaProfessor,

Department of Electronics and Communication

Engineering, K. S. R. College of Engineering,

Tiruchengode, India

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