Principal component analysis based cataract grading and classification

Cataract is a lens opacification caused by protein denaturation which leads to a decrease in vision and even results in complete blindness at later stages. The concept of a classification system of automatic cataract detection based on retinal fundus images has been proposed in previous research work which consists of fundus image preprocessing, feature extraction and the building of classifier. This paper proposes to make use of the method of PCA (principal component analysis) to reduce the dimensionality of two sets of features extracted from fundus images which are wavelet features and sketch features, respectively. We find the classification accuracy rate based on new features after PCA transformation is nearly the same with the original ones, but the computation cost can really be decreased a lot. Experiment results provide a bright foresight in later practical application of classification system of automatic cataract detection.