Cataract Image Analysis Using Neural Network Classifier

This research will demonstrate the application of Artificial Neural Network (ANN) technology inthe classification and grading of cataract images. The long-term objectives include the development andcommercialization of a low-cost instrument for automatic diagnosis of cataracts. The major aim is toprovide a standard and objective method to grade and classify different types of cataract.@ The cataractimages are first feature extracted to produce descriptors that characterize the opacity severity andopacity relative shape. The grouping of opaque regions is based on the histogram uniformity. Therelative shape analysis is based on the percentage of opacity in circular rings. These features willbecome the inputs to an Artificial Neural Network (ANN) using the back-propagation paradigm. Theoutputs of the ANN include the cataract gradings and types. The ANN classifier is first trained using atraining set of cataract images of various types. After training, the performance of the ANN is evaluatedusing a test set of images and compared with the LOCSII or LOCSIII. The results in Phase I will becomethe foundation for further work in Phase II.