Retinal Image Analysis using Neural Networks and Clustering Algorithms

This book specifies about 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. It plays important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. The Energy distributions over wavelet sub bands are applied to find these important texture features. It uses a technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. 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 operation.