Detection of Diabetic Retinopathy Diseases using …...2019/09/07 · is called its 4-neighbors....

International Journal on Future Revolution in Computer Science & Communication Engineering ISSN: 2454-4248

Volume: 5 Issue: 6 123 – 130

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123 IJFRCSCE | June 2019, Available @ http://www.ijfrcsce.org

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Detection of Diabetic Retinopathy Diseases using Neural Network

Manmohan Sharma1, Er. Sandeep Singh Dhaliwal

2

1P.G. Student, Department of Computer Engineering, BMS Engineering College, Sri Muktsar Sahib, India 2Head of Deptt. Assit. Prof. CSE in Bhai Maha Singh College of Engineering, Sri Muktsar Sahib, Punjab

Abstract:- Here we address the detection of Hemorrhages and micro aneurysms in color fundus images. In pre-Processing we separate red,

green, blue color channel from the retinal images. The green channel will pass to the further process. The green color plane was used in the

analysis since it shows the best contrast between the vessels and the background retina. Then we extract the GLCM (Gray Level Co-Occurrence

Matrix) feature. In the GLCMs, several statistics information is derived using the different formulas. These statistics provide information about

the texture of an image. Such as Energy, Entropy, Dissimilarity, Contrast, Inverse difference, correlation Homogeneity, Auto correlation,

Cluster Shade Cluster Prominence, Maximum probability, Sum of Squares will be calculated for texture image. After feature Extraction, we

provide this feature to classifier. Finally it will predict about the retinal whether it is hemorrhages or micro-aneurysms . After predicting the

about the retinal image we will localize the affected place. For segmenting the localized place we will use adaptive thresholding segmentation.

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I. INTRODUCTION

Diabetic retinopathy (DR) is a vascular disease of the retina

which affects patients with diabetes mellitus. It is the

number one cause of blindness in people between the ages

of 20-64 in the United States. It is, therefore, a worthwhile

topic for all medical students to review. Diabetes mellitus is

extremely common, so it is not surprising that DR affects

3.4 percent of the population (4.1 million individuals). Of

the millions of people with DR, nearly one-fourth have

vision-threatening disease (AAO 2008). The likelihood of

developing diabetic retinopathy is related to the duration of

the disease. Type 2 diabetes has an insidious onset and can

go unnoticed for years. As a result, patients may already

have DR at the time of diagnosis. Type 1 diabetics, on the

other hand, are diagnosed early in the course of their

disease, and they typically do not develop retinopathy until

years after the diagnosis is made. The risk of developing

retinopathy increases after puberty. Twenty years after the

diagnosis of diabetes, 80% of type 2 diabetics and nearly all

type 1 diabetics show some signs of retinopathy (Klein

1984a, Klein 1984b). While these numbers are eye-opening,

diabetics can decrease their risk of retinopathy and slow the

progression of the disease after it has begun with tight

glucose control (DCCTRG 1993).

Figure 1: Normal Fundus

Figure 2: Diagram of Normal Eye

Anatomy

The retina is a multi-layered sheet composed of neurons,

photoreceptors, and support cells. It is one of the most


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metabolically active organs in the body, and as a result, it is

extremely sensitive to ischemia and nutrient imbalances

(Frank 2004). A perfused retina is a happy retina.

Classification

NPDR

Symptoms

Screening

Examination

Treatment Options

Images and pictures

To formulate the adjacency criterion for connectivity, we

first introduce the notation of neighborhood. For a pixel p

with the coordinates (x,y) the set of pixels given by:

is called its 4-neighbors. Its 8-neighbors are defined as

From this we can infer the definition for 4- and 8-

connectivity:

Two pixels p and q, both having values from a set V are 4-

connected if q is from the set and 8-connected if q is

from .

A set of pixels in an image which are all connected to each

other is called a connected component. Finding all

connected components in an image and marking each of

them with a distinctive label is called connected component

labeling. An example of a binary image with two connected

components which are based on 4-connectivity can be seen

in Figure 1. If the connectivity were based on 8-neighbors,

the two connected components would merge into one.

Figure 3 :Two connected components based on 4-

connectivity.

Some Applications:

Image processing has an enormous range of applications;

almost every area of science and technology can make use

of image processing methods. Here is a short list just to give

some indication of the range of image processing

applications.

1. Medicine

• Inspection and interpretation of images obtained

from X-rays, MRI or CAT scans,

analysis of cell images, of chromosome

karyotypes.

2. Agriculture

• Satellite/aerial views of land, for example to

determine how much land is being used for different

purposes, or to investigate the suitability of different regions

for different crops,

• inspection of fruit and vegetables distinguishing

good and fresh produce from old.

3. Industry

• Automatic inspection of items on a production line,

• inspection of paper samples.

4. Law enforcement

• Fingerprint analysis,

• sharpening or de-blurring of speed-camera images.

II. LITERATURE SURVEY

Automated grading has the potential to improve the

efficiency of diabetic retinopathy screening services. While

disease/no disease grading can be performed using only

micro aneurysm detection and image-quality assessment,

automated recognition of other types of lesions may be

advantageous. This study investigated whether inclusion of

automated recognition of exudates and hemorrhages

improves the detection of observable/referable diabetic

retinopathy. Automated detection of exudates and

hemorrhages improved the detection of observable/referable

retinopathy.[1]

To develop a technique to detect micro aneurysms

automatically in 50 degrees digital red-free Fundus

photographs and evaluates its performance as a tool for

screening diabetic patients for retinopathy. Candidate micro

aneurysms are extracted, after the image has been modified

to remove variations in background intensity, by algorithms


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that enhance small round features. Each micro aneurysm

candidate is then classified according to its intensity and size

by the application of a set of rules derived from a training

set of 102 images. An automated technique was developed

to detect retinopathy in digital red-free Fundus images that

can form part of a diabetic retinopathy screening

programme. It is believed that it can perform a useful role in

this context identifying images worthy of closer inspection

or eliminating 50% or more of the screening population who

have no retinopathy.[2]

The robust detection of red lesions in digital color Fundus

photographs is a critical step in the development of

automated screening systems for diabetic retinopathy. In this

paper, a novel red lesion detection method is presented

based on a hybrid approach, combining prior works by

Spencer et al. (1996) and Frame et al. (1998) with two

important new contributions. The first contribution is a new

red lesion candidate detection system based on pixel

classification. Using this technique, vasculature and red

lesions are separated from the background of the image.

After removal of the connected vasculature the remaining

objects are considered possible red lesions. Second, an

extensive number of new features are added to those

proposed by Spencer-Frame.

III. PROBLEM FORMULATION AND

OBJECTIVE

Images are considered as one of the most important medium

of conveying information. The main idea of the image

segmentation is to group pixels in homogeneous regions and

the usual approach to do this is by „common feature.

Features can be represented by the space of color, texture

and gray levels, each exploring similarities between pixels

of a region.

Segmentation Algorithms

Image segmentation is the first step in image analysis and

pattern recognition. It is a critical and essential component

of image analysis system, is one of the most difficult tasks

in image processing, and determines the quality of the final

result of analysis. Image segmentation is the process of

dividing an image into different regions such that each

region is homogeneous. A.

K-means Clustering

K-Means algorithm is an unsupervised clustering algorithm

that classifies the input data points into multiple classes

based on their inherent distance from each other.[3]

Finally, this algorithm aims at minimizing an objective

function, in this case a squared error function [4].

Diabetic Retinopathy (DR) is one of the leading

causes of blindness in the industrialized world.

Early detection is the key in providing effective

treatment.

However, the current number of trained eye care

specialists is inadequate to screen the increasing

number of diabetic patients.

In recent years, automated and semi-automated

systems to detect DR with color fundus images

have been developed with encouraging, but not

fully satisfactory results.

IV. DESIGN METHODOLOGY

The proposed work is planned to be carried out in

the following manner

Fig 4: Working model of proposed system

Phase 1

Image Pre-processing

Images are enhanced by sharpening and removing

unwanted outliers.

Fig 5: Image processing

Phase 2

Segmentation

Image will be segmented to fetch out the image edges

and then detected all required parameters


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Fig 6: Segmentation

Phase 3

Recognition and Classification

Ones the image is segmented it can be tested to

recognize it first and then classify it into original or

fraud image using SVM algorithm.

Fig. 7 SVM Algorithm

In the proposed work, we will develop a system to

detect fraud coins and notes currency for Indian Notes.

Clustering will be done using k-means algorithm.

Recognition and classification will be done using SVM

algorithm.

K-means Algorithm

k-means clustering is a method of vector quantization,

originally from signal processing, that is popular for

cluster analysis in data mining. k-means clustering aims

to partition n observations into k clusters in which each

observation belongs to the cluster with the nearest

mean, serving as a prototype of the cluster.

SVM Algorithm

In machine learning, support vector machines (SVMs,

also support vector networks) are supervised learning

models with associated learning algorithms that analyze

data used for classification and regression analysis.

V. DESIGN METHODOLOGY

Snapshots :

Fig.8 Front UI of the system.

In this form we have provided user with different GUI

components for accessing the proposed system.In above

form we have provided six buttons with four plots.

Fig.9 Uploading original image.

By clicking on input image we browse different fundus

images from the dataset.As you can see the image is then

plotted in of the respective slots provided in top left

corner.For capturing the image we have used imread

function from MATLAB and then we have used plot

function to display it on the screen.

Fig.10 Preprocessing of original image


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By clicking on preprocessing the original image is converted

into gray scale and then Red Green and Blue channel are

extracted. The image is then displayed on the form using

implot function.

Fig.11 Feature Extraction

In feature extraction we use feature extraction algorithm for

extracting all the parameter from the processed image. This

parameter is consist of brightness, entropy, darkness,

gamma, and contrast.

Fig.12 Detection of retinopathy disease.

For detecting the disease in retina classification algorithm is

used to comapare the input image with the dataset images.

The algorithm compares all the features of input image

using cofusion matrix.

Fig.13 Detected part of hemorrhages

In this figure the detected part of hemorrhages is shown

using K-means theory.

Fig.14 Analysis of result.

This figure shows the percentage of data match in input

image and dataset. All the parameters are shown in bottom

right plot and rate of matching is also displayed.


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Microanerysm:

Fig.15 Front UI of the system.

In this form we have provided user with different GUI

components for accessing the proposed system.In above

form we have provided six buttons with four plots.

Fig.16 Uploading original image.

By clicking on input image we browse different fundus

images from the dataset.As you can see the image is then

plotted in of the respective slots provided in top left

corner.For capturing the image we have used imread

function from MATLAB and then we have used plot

function to display it on the screen.


By clicking on preprocessing the original image is converted

into gray scale and then Red Green and Blue channel are

extracted. The image is then displayed on the form using

implot function.


Fig.19 Detection of retinopathy disease.

For detecting the disease in retina classification algorithm is

used to comapare the input image with the dataset images.

The algorithm compares all the features of input image

using cofusion matrix.


Volume: 5 Issue: 6 123 – 130

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Fig.20 Detected part of Microneurysm

In this figure the detected part of hemorrhages is shown

using K-means theory.

VI. RESULTS AND DISCUSSION

Performance parameter for evaluation:

In pattern recognition, information retrieval and binary

classification, precision (also called positive predictive

value) is the fraction of relevant instances among the

retrieved instances, while recall (also known as sensitivity)

is the fraction of relevant instances that have been retrieved

over the total amount of relevant instances. Both precision

and recall are therefore based on an understanding and

measure of relevance.

Precision can be seen as a measure of exactness or quality,

whereas recall is a measure of completeness or quantity.

Table 1. Performance Evaluation

Test

Result

Present

Absent

Positive True Positive (TP) False Positive (FP)

Negative False Negative

(FN)

True Negative

(TN)

𝑇𝑃

𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (Precision)=

________________________________________________

______

𝑇𝑃+𝐹P

𝑇P

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦

(Recall)=_________________________________________

______________

𝑇P+𝐹𝑃+TN+FN

Table 2 shows the sensitivity and specificity of various

grades of images from the dataset images and the results are

also compared with the expert opinion. Out of 32 sever

images all 32 is detected as sever, out of 18 moderate

images all 18 are detected as moderate, out of 24 mild

images, 3 images were detected as healthy and out of 26

healthy images 3 images are detected as mild. Figure 8

shows the graph of Sensitivity and Specificity of Grade 0,

Grade1, Grade 2 and Grade 3 images from the dataset.

The average sensitivity and specificity for the proposed

ADDR is 94.44% and 87.5% respectively. Table 5 shows

the comparison results with the other papers.

Table 2. Grading of fundus image analysis

system on the Dataset

DR

Grade

Number

of

images

Sensitivity Specificity

Grade 0 26 89.65% 100%

Grade 1 24 100% 88.88%

Grade 2 18 100% 100%

Grade 3 32 100% 100%

VII. CONCLUSION AND FUTURE SCOPE

Conclusion

Prolonged diabetes leads to DR, where the retina is damaged

due to fluid leaking from the blood vessels. Usually, the

stage of DR is judged based on blood vessels, exudes,

hemorrhages, microaneurysms and texture. In this paper, we

have discussed different methods for features extraction and

automatic DR stage detection. An ophthalmologist uses an

ophthalmoscope to visualize the blood vessels and his or her

brain to detect the DR stages. Recently digital imaging

became available as a tool for DR screening.

Future Scope

A main weakness of the algorithm arises from the fact that

the algorithm depends on vessel detection. This indicates the

further necessity of improving the robustness of this task.

Hemorrhages detection could be also added to the system in

order to increase its ability to verify the degree of diabetic

retinopathy.

https://en.wikipedia.org/wiki/Pattern_recognition

https://en.wikipedia.org/wiki/Information_retrieval

https://en.wikipedia.org/wiki/Binary_classification



https://en.wikipedia.org/wiki/Positive_predictive_value



https://en.wikipedia.org/wiki/Sensitivity_and_specificity

https://en.wikipedia.org/wiki/Relevance


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Detection of Diabetic Retinopathy Diseases using …...2019/09/07 · is called its 4-neighbors....

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