International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume4,March 2017,

Topic : Increased Learning In Retinal Blood Vessel Segmentation Approach Based on Fuzzy-C Means Clustering and Mathematical Morphology

Authors:Naluguru Udaya Kumar || Tirumala Ramashri

Abstract:One of the major diseases that cause severe threat to the eye is named as Diabetic retinopathy. It creates thereby blindness among the people in the small age itself. The retinal image processing is used for analyzing and detecting the disease with the retinal blood vessels. By analysing and detecting of vasculature structures in retinal images, The most widely recognized manifestations of diabetic retinopathy incorporate cotton fleece spots, hemorrhages, hard exudates and enlarged retinal veins. A patient with diabetic retinopathy malady needs to experience intermittent screening of retina.we can early detect the diabetes in advanced stages by comparison of its states of retinal blood vessels. In this paper, we present blood vessel segmentation approach, which can be used in computer based retinal image analysis to extract the retinal image vessels. Mathematical morphology and FCM clustering are used to segment the vessels. To enhance the blood vessels and suppress the background information, we perform smoothing and sharpning operation on the retinal image using mathematical morphology. Then the enhanced image is segmented using FCM-means clustering algorithm.The main focus of this proposed work is to design the algorithm based on segmentation with clustering,for detection of Retinal blood vessel with the help of MATLAB with maximum accuracy.The proposed approach is tested on the DRIVE dataset and is compared with alternative approaches.Experimental results obtained by the proposed approach showed that it is effective as it achieved best accuracy of 98.23%.

Keywords: Blood vessel Extraction, FCM Clustering, Mathematical morphology, Vessel segmentation

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DOI: 01.1617/vol4iss3pid027


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