Background Estimation Coarse Segmentation

TELKOMNIKA Retinal Image Figure 1.

2.1.1 Background Estimation

A color retinal imag background. This dark backg feature extraction and lesion d is important to remove the bac We present a local me creates a binary background s greater than threshold value, belongs to background. The algorithm for the b Step 1 : Divide the acquired re Step 2 : Compute the local me Step 3 : Use local mean value stdI using equation 2 Step 4 : Select threshold value Step 5 : for each pixel, Calculate stdI T if true , add pixe if false , add pixe end_for 2.1.2 Noise Segmentation M Noise in color retinal distorted. Both seem to exi illumination is usually adequa located near the edge of the r abnormality detection. That is abnormalities. In our technique, w area and it is applied on reti steps i.e. feature extraction convert RGB Red, Green, Blu the way a human experience space [19]. The RGB retinal im ISSN: 1693-6930 e Preprocessing: Background and Noise Segmenta 1. Flow Diagram of Retinal Image Preprocessing ion age consists of a semi circular region of int kground is initially never really black. It is not ne n detection algorithms on this area and it consume ackground from input retinal image. mean and variance based method [19] for backgro d segmentation mask by applying a threshold on std e, the block is considered as original retinal image e background extraction mask is as below: retinal image into non-overlapping blocks ean value MI using equation 1 ue computed in step 2 to compute the local standar n 2 lue empirically Threshold? ixel in original retinal image area pixels ixel in background area pixels Mask al image is normally due to noise pixels and pixe exist in regions where illumination has been in quate in the center of the image, poor image qu e retinal image. Regions with poor image quality m t is why they should be detected and removed be we create binary noise segmentation mask which i etinal image to ensure not to process the noisy a n and abnormality detection. In this segmentatio Blue retinal image into HSI color space because fi ces colors and secondly noise can be easily remo l image is converted into HSI color space using equ ntation Ibaa Jamal 539 interest on a dark necessary to apply mes more time so it round estimation. It stdI. If the stdI is ge area otherwise it 1 dard deviation value 2 ixels whose color is inadequate. Since quality regions are may cause errors in before detection of h includes the noisy y area in upcoming tion technique, we firstly it is closer to moved in HSI color quations 3, 5 and 6 TELKOMNIKA Vol. 10, No. 3 540 . where here R, G and B represent RE The algorithm for noise remov Step 1 : Divide the input retina Step 2 : Use histogram equaliz Step 3 : Use a 3x3 median filte Step 4 : Convert the equalized Step 5 : Calculate N noise fac Step 6 : Select a threshold valu Step 7 : for each pixel, Calculate NI Thres if true , add pixel in no if false , add pixel in end_for Figure 2 shows the images. These segmentation removed in fine segmentation. Figure 2. Coarse segmentatio

2.2 Fine Segmentation