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Accurate Optic Disc and Cup Segmentation from Retinal Images Using a Multi-Feature Based Approach for Glaucoma Assessment

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College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
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School of Computing, Shenyang Aerospace University, Shenyang 110136, China
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College of Basic Medical Science, China Medical University, Shenyang 110122, China
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Authors to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1267; https://doi.org/10.3390/sym11101267
Received: 12 September 2019 / Revised: 28 September 2019 / Accepted: 5 October 2019 / Published: 10 October 2019
Accurate optic disc (OD) and optic cup (OC) segmentation play a critical role in automatic glaucoma diagnosis. In this paper, we present an automatic segmentation technique regarding the OD and the OC for glaucoma assessment. First, the robust adaptive approach for initializing the level set is designed to increase the performance of contour evolution. Afterwards, in order to handle the complex OD appearance affected by intensity inhomogeneity, pathological changes, and vessel occlusion, a novel model that integrates ample information of OD with the effective local intensity clustering (LIC) model together is presented. For the OC segmentation, to overcome the segmentation challenge as the OC’s complex anatomy location, a novel preprocessing method based on structure prior information between the OD and the OC is designed to guide contour evolution in an effective region. Then, a novel implicit region based on modified data term using a richer form of local image clustering information at each point of interest gathered over a multiple-channel feature image space is presented, to enhance the robustness of the variations found in and around the OC region. The presented models symmetrically integrate the information at each point in a single-channel image from a multiple-channel feature image space. Thus, these models correlate with the concept of symmetry. The proposed models are tested on the publicly available DRISHTI-GS database and the experimental results demonstrate that the models outperform state-of-the-art methods. View Full-Text
Keywords: glaucoma; retinal image; optic disc; optic cup; image segmentation; active contour model (ACM) glaucoma; retinal image; optic disc; optic cup; image segmentation; active contour model (ACM)
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Gao, Y.; Yu, X.; Wu, C.; Zhou, W.; Wang, X.; Zhuang, Y. Accurate Optic Disc and Cup Segmentation from Retinal Images Using a Multi-Feature Based Approach for Glaucoma Assessment. Symmetry 2019, 11, 1267.

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