Glaucomatous Image Classification Using Wavelet Based Energy Features And PNN

Glaucoma is the second leading cause of blindness worldwide. As glaucoma progresses, more optic nerve tissue is lost and the optic cup grows which leads to vision loss. Glaucomatous image classification can be efficiently performed using the texture features of an image. This paper focused on recent Glaucoma Classification techniques in Computer-Aided Diagnosis(CAD).Feature extraction is necessary to reduce the original dataset by measuring certain properties to make decision process easier during classification. Texture has been widely involved in many real life applications such as remote sensingbiomedical image processing, content based image retrieval. Representatives techniques and algorithm are explained to provide good idea about classification of fundus image which deals with 1 how medical images could be analyzed, processed, feature extracted by 2D-DWT methods and classified by Neural Network(NN), 2 how the techniques above could be expanded further to resolve problem relevant to Glaucoma image.