The DIARETDB1

database consists of 89 color fundus images of which 84 contain at least mild

non-proliferative signs (Microaneurysms) of the diabetic retinopathy, and 5 are

considered as normal as shown in the Fig. 5 which do not contain any signs of

the diabetic.

The DRIVE dataset

consists of 40 images which has been divided into a training and a test set,

both containing 20 images as shown in Fig. 6

The 1200 eye fundus

color numerical images of the posterior pole for the MESSIDOR database were

acquired by 3 ophthalmologic departments using a color video 3CCD camera. The

images were captured using 8 bits per color plane at 1440*960, 2240*1488 or

2304*1536 pixels. 800 images were acquired with pupil dilation and 400 without

dilation. The 1200 images are packaged in 3 sets, one per ophthalmologic

department. Each set is divided into 4 zipped sub sets containing each 100

images in TIFF format and an Excel file with medical diagnoses for each image.

E-ophtha is a

database of color fundus images especially designed for scientific research in

Diabetic Retinopathy (DR) as shown in the Fig. 8. The database is made of

retinal images with different types of lesions (exudates and microaneurysms)

manually annotated by ophthalmology experts. E-ophtha is made of two sub

databases named e-ophtha-MA (Microaneurysms), and e-ophtha-EX (Exudates) Images

of healthy patients with no lesion are also provided in the two databases.

Image Preprocessing:

Several preprocessing

methods are used to implement the initial processing, namely as gamma

correction, green channel extraction, histogram equalization, etc. In the

proposed method the techniques used are Gaussian filter and CLAHE.

Gaussian filtering is

used to blur images and remove noise and detail. The Standard deviation of the

Gaussian function plays an important role in its behavior. The values located

between +/- ? account for 68% of the set, while two standard deviations from

the mean (blue and brown) account for 95%, and three standard deviations (blue,

brown and green) account for 99.7%. This is very important when designing a

Gaussian kernel of fixed length. The Gaussian function is used in numerous

research areas:

·

It defines a probability distribution for noise or data.

·

It is a smoothing operator.

·

It is used in mathematics.

CLAHE operates on

small regions in the image, called tiles, rather than the entire image that is

to be processed. Each tile’s contrast is enhanced, so that the histogram of the

output region approximately matches the histogram specified by the Distribution

parameter. The neighboring tiles are then combined using bilinear interpolation

to eliminate artificially induced boundaries. The contrast, especially in

homogeneous areas, can be limited to avoid amplifying any noise that might be

present in the image.

J=adapthisteq(I,param1,val1,param2,val2…) specifies any of the

additional parameter/value pairs listed in the following table. Parameter names

can be abbreviated, and case does not matter. CLAHE is an adaptive contrast

enhancement method. It is based on AHE, where the histogram is calculated from

the contextual region of the pixel.

Wavelet Transformation:

In continuous wavelet

transforms, a given signal of finite energy is projected on a continuous family

of frequency bands (or similar subspaces of the Lp function space L2(R) ). For

instance the signal may be represented on every frequency band of the form f,

2f for all positive frequencies f > 0. Then, the original signal can be

reconstructed by a suitable integration over all the resulting frequency

components.

In discrete wavelet

transforms, it is computationally impossible to analyze a signal using all

wavelet coefficients, so one may wonder if it is sufficient to pick a discrete

subset of the upper half plane to be able to reconstruct a signal from the

corresponding wavelet coefficients. One such system is the affine system for

some real parameters a > 1, b > 0.

Among the various

wavelets, Mathieu wavelet is used in the proposed method. The Mathieu equation

is a linear second-order differential equation with periodic coefficients.

“Mathieu functions are applicable to a wide variety of physical phenomena,

e.g., diffraction, amplitude distortion. Mathieu’s equation is related to the

wave equation for the elliptic cylinder. The Mathieu equation is given by,

+(a-2q cos 2?)y=0

Mathieu wavelets can

be derived from the low pass reconstruction filter by the cascade algorithm.

Infinite Impulse Response filters (IIR filter) should be use since Mathieu

wavelet has no compact support.

§ Even periodic

solution

cer(?,q) =? Ar,m cos m? for a =ar(q)

m

·

Odd periodic solution

ser(?,q) =? Ar,m sin m? for a =ar(q)

m

which is known as the dilation or refinement equation, is the

chief relation determining a Multiresolution Analysis (MRA).

H(?)=

is the transfer

function of the smoothing filter.

G(?)=

is the transfer function of the detail filter.

The transfer function of the “detail filter” of a

Mathieu wavelet is,

Gv(? =

The transfer function

of the “smoothing filter” of a Mathieu wavelet is,

Feature Extraction:

Statistical features

are extracted from the transformed image and their correlation with the

detection of microaneurysms automatically was studied. Various features

relating size and shape are extracted in feature. Also many studies state that

gabor features are also used in analysis and detection of diabetic retinopathy

at its early stage of occurrence. Hence extraction of the features from the

transformed image should be made in a relative manner.

Classification:

Once microaneurysms are detected they are classified on the

basics of the number of microaneurysms present in the retinal image as shown in

Table 1.

Table

1 Criteria Used for Grading the Diabetic Retinopathy

DR Stage

No. of Microaneurysms

Grade 0(no DR)

MA =0

Grade 1(mild)

1?MA?5

Grade 2 (moderate)

5?MA?15

Grade 3(severe)

MA?15

D.SOFTWARE

SPECIFICATION

MATLAB is a high-level language and

interactive environment for numerical computation, visualization, and

programming. Using MATLAB, you can analyze data, develop algorithms, and create

models and applications. The language, tools, and built-in math functions

enable you to explore multiple approaches and reach a solution faster than with

spreadsheets or traditional programming languages, such as C/C++ or Java. You

can use MATLAB for a range of applications, including signal processing,etc.,

More than a million engineers and scientists in industry and academic use

MATLAB, the language of technical computing.

IV.

RESULT AND DISCUSSION

A.EXISTING METHOD RESULTS

The comparison of the

features between the microaneurysms and non microaneurysms in terms of

mathematical values which are features extracted from the retinal image which

are taken from the publically available database DIARETDB1, such the first

order statistics and DWT features is listed below as shown in the Table 2.

Table 2

Performance of Existing Method

Features

MA

Non MA

First Order Statistics

Mean

8.6815817

0

Standard deviation

16.872177

0

DWT features

Correlation

54.600213

0

Homogeneity

0.2830491

0

Energy

0.0281733

0

Contrast

36.474312

0

Sumvariance

178.493

0

Entrophy

0.5039248

-2.22E-16

B.RESULTS OF PROPOSED METHOD

The GUI display model of detecting the

microaneurysms and classifying them in an easy manner. This model contains of

two sections namely the following:

·

Training Section

·

Testing Section

Fig. 9 Input Image for Training Fig. 10 Preprocessed Image

Fig. 9 is the initially used image for

training the network. Fig. 10 shows the output of pre-processed image that is

obtained by using CLAHE filtering, also this filter is used in many other

images for preprocessing such as brain waves, etc. It improves the quality of

an image by enhancing the contrast.

Fig. 11 Mathieu

decompossed Image Fig. 12 Mathieu

reconstructed Image

The Mathieu wavelet decomposed image by using

the CLAHE filter in preprocessing is shown in the Fig. 11. It is a segmentation

transformation method for the detection of microaneurysms.

The image is reconstructed from the obtained

decomposed image, as shown in the Fig. 12.

Fig. 13 Input Test Image

Fig. 13 is the test image used in the

analsis, any kind of image can be used for testing in the process.

Fig. 14 a)Grey Level

Image b) Preprocessed Image

The test image is moved to the next

processing level and they are converted as the Gray level image and next

processing level is the preprocessing and the output images are resulted as in

the Fig. 14 a) & b) respectively.

Fig. 15 Decomposed Image Fig. 16 Reconstructed Image

The

Decomposed image from the test set is

shown in the Fig. 15. The Fig. 16 shows the reconstructed image that is

chosen from the test set. The output is taken for 20 images from 4 different

databases, and the final network performance is obtained to be of merely 100%

of Accuracy and Specificity. The output window that shows the classification of

diabetic retinopathy is shown further.