Glaucoma Detection Using Dwt Based Energy Features and Ann Classifier
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 5
Abstract
Abstract: Glaucoma is a disease in which fluid pressure in the eye increases continously, and damage the optic nerve and leads to vision loss.It is the second leading cause of blindness. For identification of disease in human eyes we are using clinical decision support system which is based on retinal image analysis technique,that used to extract structure,contextual or texture features. Texture features within images which gives accurate and efficicent glaucoma classification.For finding this texture features we use energy distribution over wavelet subband.In this paper we focus on fourteen features which is obtained from daubecies(db3),symlets(sym3),and biorthogonal bio3.3,bio3.5,and bio3.7).We propose a novel technique to extract this energy signatures using 2-D wavelet transform and passed these signatures to different feature ranking and feature selection strategies.The energy obtained from detailed coefficent are used to classify normal and glaucomatous image with high accuracy. This will be classified using support vector machines, sequential minimal optimization, random forest, naive Bayes and artificial neural network.We observed an accuracy of 94% using the ANN classifier.Performance graph is shown for all classifiers.Finally the defected region founded by segmentation and this will be post processed by morphological processing technique for smoothing operation.
Authors and Affiliations
Nitha Rajandran
Keywords
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