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Robust and accurate optic disk localization using vessel symmetry line measure in fundus images

Panda R., Puhan N. B., Panda G.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 3

466--476

Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line (VSL) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center localization involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases.

New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation

Panda R., Puhan N. B., Panda G.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 1

119--129

Automated retinal vessel segmentation plays an important role in computer-aided diagnosis of serious diseases such as glaucoma and diabetic retinopathy. This paper contributes, (1) new Binary Hausdorff Symmetry (BHS) measure based automatic seed selection, and (2) new edge distance seeded region growing (EDSRG) algorithm for retinal vessel segmentation. The proposed BHS measure directly provides a binary symmetry decision at each pixel without the computation of continuous symmetry map and image thresholding. In a multiscale mask, the BHS measure is computed using the distance sets of opposite direction angle bins with sub-pixel resolution. The computation of the BHS measure from the Hausdorff distance sets involves point set matching based geometrical interpretation of symmetry. Then, we design a new edge distance seeded region growing (EDSRG) algorithm with the acquired seeds. The performance evaluation in terms of sensitivity, specificity and accuracy is done on the publicly available DRIVE, STARE and HRF databases. The proposed method is found to achieve state-of-the-art vessel segmentation accuracy in three retinal databases; DRIVE- sensitivity (0.7337), specificity (0.9752), accuracy (0.9539); STARE-sensitivity (0.8403), specificity (0.9547), accuracy (0.9424); and HRF-sensitivity (0.8159), specificity (0.9525), accuracy (0.9420).