NDLI: Segmentation of Optic Nerve Head for Glaucoma Detection using Fundus images

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Segmentation of Optic Nerve Head for Glaucoma Detection using Fundus images

Content Provider	Semantic Scholar
Author	Babu, G. Shivaji Kumar, R. Sathish
Copyright Year	2015
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.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.biomedpharmajournal.org/dnload/Ganesh-Babu-T-R-R-Sathishkumar-and-RengarajVenkatesh-/BPJV07I02P697-705.pdf
Language	English
Access Restriction	Open
Subject Keyword	Abducens Nerve Diseases Artificial neural network Backpropagation Biological Neural Networks Blood Vessel Cluster analysis Eye Glaucoma Image analysis Nerve Tissue Optic Disk Optic Nerve (GCHQ) Region of interest Retina Retinal scan Sensor Software propagation Structure of optic cup Support Vector Machine Thresholding (image processing) Virtual retinal display statistical cluster
Content Type	Text
Resource Type	Article

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