Image based grading of nuclear cataract by SVM regression

Abstract

Cataract is one of the leading causes of blindness worldwide. A computer-aided approach to assess nuclear cataract automatically and objectively is proposed in this paper. An enhanced Active Shape Model (ASM) is investigated to extract robust lens contour from slit-lamp images. The mean intensity in the lens area, the color information on the central posterior subcapsular reflex, and the profile on the visual axis are selected as the features for grading. A Support Vector Machine (SVM) scheme is proposed to grade nuclear cataract automatically. The proposed approach has been tested using the lens images from Singapore National Eye Centre. The mean error between the automatic grading and grader's decimal grading is 0.38. Statistical analysis shows that 97.8% of the automatic grades are within one grade difference to human grader's integer grades. Experimental results indicate that the proposed automatic grading approach is promising in facilitating nuclear cataract diagnosis.