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\title{Detection of Optic Disc and Hard Exudates using TWIN PLANE Gradient Windowing Technique}
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\begin{document}

             \author[1]{G.Ferdic Mashak  Ponnaiah}

             \author[2]{Dr.S.Santhosh  Baboo}

             \affil[1]{  BHARATHIAR UNIVERSITY}

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\date{\small \em Received: 14 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

\maketitle


\begin{abstract}
        


Detection of Optic disc (OD) in a fundus image is a foremost important step in the process of screening the diabetic retinopathy [1,4,18]. Hard Exudates detection algorithms usually find lot of false positives since the intensity and color distribution of OD will much resemble that of a Hard Exudates region [15,16]. So, most of the Exudates detection algorithms will miss classify the pixels at the OD region as Hard Exudates [2].In our previous works, we used Genetic Algorithm(GA) [23,24] to find the OD location and size and reduced overall time, even doing the search on the entire problem space and also removed false hard exudates. In this work we are improving Hard Exudates detection accuracy using gradient index mapping technique applied on two similar planes of the RGB. The new algorithm is termed as TWINGRAB use the two planes of the RGB after proper gradient projection. The database used for this preprocessing is DIARETDB1 [10].

\end{abstract}


\keywords{diabetic retinopathy, hard exudates, optic disc detection.}

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\let\tabcellsep& 	 	 		 \par
Abstract-Detection of Optic disc (OD) in a fundus image is a foremost important step in the process of screening the diabetic retinopathy \hyperref[b0]{[1,}\hyperref[b3]{4,}\hyperref[b15]{18]}. Hard Exudates detection algorithms usually find lot of false positives since the intensity and color distribution of OD will much resemble that of a Hard Exudates region  {\ref [15,}\hyperref[b13]{16]}. So, most of the Exudates detection algorithms will miss classify the pixels at the OD region as Hard Exudates \hyperref[b1]{[2]}.\par
In our previous works, we used Genetic Algorithm(GA) \hyperref[b21]{[23,}\hyperref[b22]{24]} to find the OD location and size and reduced overall time, even doing the search on the entire problem space and also removed false hard exudates. In this work we are improving Hard Exudates detection accuracy using gradient index mapping technique applied on two similar planes of the RGB. The new algorithm is termed as TWINGRAB use the two planes of the RGB after proper gradient projection. The database used for this preprocessing is DIARETDB1[10].\par
Index terms: diabetic retinopathy, hard exudates, optic disc detection. 
\section[{II.}]{II.} 
\section[{Future Method a) TWINGRAB Algorithm}]{Future Method a) TWINGRAB Algorithm}\par
The steps involved in the proposed method is as follows:\par
Step1. Input the Red, Green and Blue plane.\par
Step2. Enhance the gradient of each plane.\par
Step3. Compare the two similar planes showing the optic disc location.\par
Step4. Identify the optic disc using pixel gradient and Index mapping to detect the hard exudates.\par
Step5. Find the optic disc boundary using binarization technique.\par
Step6. Mask the Optic Disk region since the hard exudates also have the same pixel.\par
Step7. After masking the Optic Disk Identify the feature hard exudate candidates. 
\section[{b) Flow Diagram c) Gradient Projection \& Windowing Technique}]{b) Flow Diagram c) Gradient Projection \& Windowing Technique}\par
The gradient is a generalization of the usual concept of derivation to find the function of many variables. Consider the function f(x1, ..., xn) which is a differentiate function of several variables called scalar field. The gradient of the scalar field is the vector of the n partial derivate of the function f. By this a vector value function is termed as vector field.\par
The function of the graph is represented by the slope of and the tangent of the image. More exactly, the gradient points in the direction of the greatest rate of increase of the function and the slope of the graph is the Introduction etecting hard exudates is a challenging process; hence there is research gap in fine tuning the process by introducing hybrid algorithms in finding the exudates which is a significant milestone to identify the diabetic retinopathy. The primary goal to achieve is a proper detection of Optic Disc which got similar pixel intensity and gradient like hard exudates. In the previous work based on the threshold value we have considered either the green plane or blue plane to detect the Optic Disc based on the colour intensity. In these work two similar planes of RGB plane is used to detect the feature candidates. A new algorithm called TWINGRAWB (TWIN PLANE INTENSITY GRADIENT WINDOWING BOUNDARY TECHNIQUE) is applied on two similar planes to find the Optic Disc and Hard Exudates. D magnitude in that direction. The tangent space equation of the graph is the non-constant coefficient of the component of the graph. Consider a surface whose height above sea level at a point (x, y) is H(x, y). The gradient of H at a point is a vector pointing in the direction of the steepest grade or slope at that point. The steepness of the slope at that point is given by the magnitude of the gradient vector. The gradient may also use to measure how a scalar field changes in other directions, than just the direction of greatest change, by making a dot product.\par
The gradient is a vector operator denoted by . It is more often applied to a real function of three variables and may be denoted as, 
\section[{?ð??"ð??" = ð??"ð??"ð??"ð??"ð??"ð??"ð??"ð??"(ð??"ð??")}]{?ð??"ð??" = ð??"ð??"ð??"ð??"ð??"ð??"ð??"ð??"(ð??"ð??")}\par
The method to calculate the Gradient is: Divide the change in height by the change in horizontal distance. It is denoted as, Gradient=Change in vertical axis(Y)/Change in horizontal axis(X) For example, The gradient for this line=3/3=1. So the gradient is equal to 1.\par
Gradient can be applied to the histogram equalized image. After equalization of the image, the gradient of the image is to be found to analyze the image edge strengths. In general gradient of the image returns gradient values with respect to both axis Gx and Gy respectively.    
\section[{e) Index Mapping Technique}]{e) Index Mapping Technique}\par
The index mapping is a kind of technique to manage the digital images colors in a limited fashion, in order to save computer resource such as memory and storage of files when doing certain operations such as speeding up display refresh and file transfers. This is a kind of vector quantization compression.\par
Index mapping technique is used in which depends on pixel value arrangement index value to be assigned. The color index is a simple numerical expression that determines the color of an object, Index maps are a type of seeking tool that allow users find a set of maps covering their regions of interest along with the name or number of the relevant map sheet. In this segmentation for each index value, color to be assigned according to the value assigned for individual color.\par
It is limited to 256 colors, which can be any 256 from the set of 16.7 million 24 bit colors. Each color used is a 24 bit RGB value. Each such image file contains its own color palette, which is a list of the selected 256 colors (or 16 colors in a smaller palette). Images are called indexed color because the actual image color data for each pixel is the index into this palette as color space. When an image is processed in this way, color information is not directly carried by the image pixel data, but is stored in a separate piece of data called palette array of color elements, in which every element, a color, is indexed by its position within the array. The image pixels do not contain the full specification of its color, but only its index in the palette. It also is done in two methods:\par
1. Dithering -Dithering means representing a color by combinations of dots of other colors. Dots is pretty much the definition. If the one exact shade of pink is not in the palette, then dots of other palette colors are mixed to simulate the color. Dithering often causes a visible and objectionable dotted or speckled image. But that simulated color may be much closer than a nearest color approximation.\par
2. Nearest color -This selection is the same as disabling dithering, or no dots. If the exact shade of pink is not in the palette, then the closest color in the palette is used, which might not be very pink at all. Sometimes exact color is not important. No dithering means no visible dots.\par
Indexation of images is a way of compressing the image without any data loss. Indexing an image will keep the resolution and color space intact, but the structure of the image will change. When an image is indexed an index table will be created. This is a list of the different colors that are used. The image itself will contain reference numbers to this index table as shown in fig \hyperref[b5]{6}. Especially when the image uses a lot the same colors, this can save a lot of space. Indexing an image won't change the way an image looks or will be printed. Each pixel will keep its original color. The above example represents the index mapping technique carried out for each image. Depends on the pixel value index value like 0,1, 2, 3 are to be assigned for each segmentation as shhown in Figure  {\ref .}6. If an image uses a lot of different colors, indexing this image will not make decrease the file size. In this case the image will not be indexed. 
\section[{f) Binarization}]{f) Binarization}\par
Binarization is used for segmentation process in which required portion can be separated from the object. Here threshold value to be fixed for the image in which value below the threshold level then it will be assigned to binary '0' and above threshold level assigned to binary '1'. Depends on this process, image to be segmented in which optical disk portion can be detected from the iris image. This process is to be performed on the index mapped image from which threshold value to be labeled to corresponding pixel value in the image. Based on labeled pixel each are grouped to segment an image as shown in Figure  {\ref .} Result and Analysis IV. 
\section[{Conclusion}]{Conclusion}\par
The TWINGRAB algorithm is simple and efficient method to detect the Optic Disc and Hard exudates in the fundus image. A proper detection of Optic Disc and Hard Exudates will help in the diagnosis of the DIABETIC RETINOPATHY eye disease in the fundus image of the eye retina which is the main cause of the vision loss in the diabetic society. This TWINGRAB algorithm may used as a diagnostic tool where the large volume of the patients to be screened and thus reduce the pain of the eye examiners in the eye hospital and eventually useful in generating accurate results of the patients and lead to proper treatment to prevent the vision loss of the diabetic eye. The Weighted Error Rate (WER) of the proposed TWINGRAB is low compare with the baseline, manual and GA based method. This can be used in health care service to prevent the vision loss by early detecting the diabetic retinopathy. Similarly this can be further enhanced to detect various symptoms in the eye fundus image. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 Figure 2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-8.png}
\caption{\label{fig_7}7 .}\end{figure}
 			\footnote{© 2014 Global Journals Inc. (US) Global Journal of Computer Science and Technology} 			\footnote{© 2014 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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