Open AccessMean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening
Author(s) -
Panda Rashmi,
Puhan N.B.,
Panda Ganapati
Publication year - 2018
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2017.0043
Subject(s) - segmentation , computer science , artificial intelligence , maxima and minima , algorithm , robustness (evolution) , image segmentation , curvature , sørensen–dice coefficient , computer vision , optic disc , glaucoma , pattern recognition (psychology) , mathematics , geometry , biochemistry , chemistry , ophthalmology , gene , medicine , mathematical analysis
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.