Automated Cataract Diagnosis

Cataracts are the cause of half of blindness and 33% of visual impairment worldwide. It causes blurred and foggy vision. A Protein layer will develop gradually in the eyes and the lens become cloudy over a long time period. By timely detection, it is possible to prevent cataract surgery in the initial stage of it. There are various Automatic Cataract detection and classification methods available today. All the Cataract detection and classification systems have 3 basic steps: Preprocessing, Feature extraction and Classification. In this paper some of the recent methods are discussed and analyzed. In the proposed methodology, the texture features are extracted using 2-D discrete wavelet transforms. The image features obtained from five different wavelet filters db3, sym3 and (Bior 3.3, Bior 3.5, Bior 3.7) are used to classify the images The z-score normalization is applied on features before classification. The features are then used in the automatic classifier such as SVM for the automatic classification.