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Automated retinal image analysis is promising screening tool for early detection of eye disease. In this automated analysis some factors need to be considered in order to get better analytical results. We present in this paper methodology to extract exact boundary of OD & Cup in digital retinal fundus images. The method starts with preprocessing of digital fundus images by contrast normalization throughout the image, and removal of blood vessels, which is major reason for distraction of finding OD candidate, using Bottom Hat Transform. Using this OD candidate we find area of interest i.e. area surrounding OD & cup. Then we apply salient object detection algorithm. In this paper, we deal with the salient object detection problem for images. Weformulate salient object detection as a binary labeling task that separates a salient object from the background since; one pays more attention to salient object in an image as compared to the background of the image. Feature extraction methods are like edge detection, thresholding, multi scale contrast,Center surround histogram. Feature maps are prepared for each of the method mentioned above and saliency is computed by a center-surround operation, self-information, orgraph-based random walk using multiple features. After normalization and linear/non-linear combination, a master map or a saliency map is computedto represent the saliency of each image pixel. Last, a few key locations on the saliency map are identified by winner-take-all, or inhibition-of-return, or othernon-linear operations. Then we highlight the boundaries.

Detection Of Optic Disc & Cup Of Digital Fundus Image Using Salient Object Detection Method For Glaucoma Detection