Abstract
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.
Highlights
Glaucoma is a degenerative and irreversible optic neuropathy, which ranks as the second most disabling and vision impairing disease worldwide [1]
The other approach employing segmentation-based reliable features such as cup-to-disc height ratio (CDR), rim area, optic disc (OD) size is found to be useful for glaucoma screening
The quantitative evaluation parameters computed on complete DRIVE, DRISHTI-GS, DIARETDB1 and MESSIDOR databases are provided in Tables 2–5, respectively
Summary
Glaucoma is a degenerative and irreversible optic neuropathy, which ranks as the second most disabling and vision impairing disease worldwide [1]. Glaucoma is a silent thief of sight as it is asymptomatic in preliminary stages; early diagnosis and treatment is the only way to prevent further retinal damage Tests such as tonometry, gonioscopy, perimetry are commonly practiced to detect glaucoma. In [7,8,9,10,11,12,13,14,15], the methods use whole image-based features that include higher-order spectral features [7,8,9,10], fractal features [11, 12], wavelet-based features [13] and texture features [14] followed by various classification strategies to accurately detect glaucomatic cases Since this approach does not require explicit segmentation, it is computationally inexpensive. Subtle OD boundary, blood vessel occlusion and intensity inhomogeneity make OD segmentation a challenging task
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