AUTOMATED DETECTION OF PATHOLOGICAL AND NON-PATHOLOGICAL MYOPIA USING RETINAL FEATURES AND DYNAMIC ENSEMBLE OF CLASSIFIERS

A computer-aided diagnostic tool for myopia detection is of paramount importance since the pathology results in irreversible damage to the eye. Unfortunately, designing of such systems is hindered by several issues such as (i) difficulties in segmentation of optic disc (OD) due to smooth variability between the OD and other retinal features, (ii) greater degree of correlation between myopia and non-myopic features, and (iii) lack of feasibility for adopting in a clinical setting. The proposed methodology is designed to address the aforementioned issues. First, blood vessels are detected and excluded. Second, the optic disc region is segmented using deformable models. Further, two classification set-ups are tested to determine the rate of accurate classification to detect myopia. Quantitative analysis is performed on the PALM dataset achieving, sensitivity, specificity, and accuracy of 90.3 %, 100 %, and 95 % respectively.