Retinal Image Analysis for Exudates Detection

This project proposes a glaucomatous image classification using texture features within images and it will be classified effectively based on feature ranking and neural network. In addition with, an efficient detection of exudates for retinal vasculature disorder analysis performed. The states of retinal blood vessels can be used to detect some diseases like diabetes. The important texture features can be found by using the Energy distributions over wavelet subbands. This system uses different wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. The energy signatures obtained from 2-D discrete wavelet transform subjected to different feature ranking and feature selection strategies. The energy obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images with very high accuracy. This performance will be done by artificial neural network model. The exudates are also detected effectively from the retina fundus image using segmentation algorithms. Finally the segmented defect region will be post processed by morphological processing technique for smoothing