Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening

[摘要] Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

[学科分类] 肠胃与肝脏病学

[关键词] image texture;medical image processing;image segmentation;tumours;cancer;blood vessels;neurophysiology;biomedical optical imaging;mean curvature;texture constrained composite weighted random walk algorithm;optic disc segmentation;cup-disc ratio-based glaucoma screening;fundus imaging;OD boundary;blood vessel occlusion;random walk algorithm;Gabor texture energy features;composite weight function;edge weights;deformable model-based OD segmentation techniques;curve initialisation;local energy minima problem;DRIVE database images;DIARETDB1 database images;DRISHTI-GS database images;MESSIDOR database images;mean absolute distance;overlapping ratio;dice coefficient;quantitative performance