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Segmentation of retinal area by adaptive SLIC Superpixel

Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to be segment the retinal area. In this paper, we have proposed the preprocessing and segmentation. Preprocessing is done on the original image using Gamma Normalization which brings the intensity value in a particular range. Gamma Normalization helps to enhance the image which gives better detail of the image. Then the segmentation is performed on the Gamma Normalized image. Segmentation is done by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Adaptive SLIC has been used for superpixalation. Adaptive SLIC is the algorithm for Superpixel generation which is use to set parameter adaptively for each Superpixel. Superpixel is used to decrease the difficulty of image processing task and supply suitable primitive image pattern. Our method is more faster, improve segmentation performance, memory efficient and straightforward to extent to Superpixel generation than other methods.