Segmentation of Optic Nerve Head for Glaucoma Detection using Fundus images
Ganesh Babu. T. R1, R. Sathishkumar2 and Rengaraj Venkatesh31Electronics and Communication, Shri Andal Alagar College of Engineering, India 2Electronics and Communication, Sri Venkateswara College of Engineering, India 3Chief Medical Officer, Aravind Eye Hospital, India.
Abstract: Automatic retinal image analysis is emerging as an important screening tool for detection Glaucoma, which causes the blindness. Segmentation of optic disc, optic cup neuro retinal rim and retinal blood vessels provide important parameters for detecting and tracking this disease. This paper proposes an approach for the automatic detection of optic disc and optic cup using spatially weighted fuzzy c-mean clustering method and region of interest (ROI) based segmentations. It can be used to automatically segment neuro retinal rim. The blood vessels in the optic disc region are segmented by local entropy thresholding. The ratio of area of blood vessels and neuro retinal rim in the inferior Superior side to area of blood vessels and neuro retinal rim in the inferior superior nasal temporal (ISNT) side is combined with the CDR for the classification of fundus image as normal or glaucoma by using back propagation neural network (BPNN) and support vector machine (SVM) classifier. A batch of 300 retinal fundus images are used to assess the performance and a classification rate of 96 % is achieved.
Keywords: Glaucoma; Spatially weighted fuzzy c – mean clustering; Local entropy thresholding; Support vector machine; Neural network classifier Back to TOC