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http://hdl.handle.net/10397/105580
Title: | Retinal vessel segmentation using minimum spanning superpixel tree detector | Authors: | Sheng, B Li, P Mo, S Li, H Hou, X Wu, Q Qin, J Fang, R Feng, DD |
Issue Date: | Jul-2019 | Source: | IEEE transactions on cybernetics, July 2019, v. 49, no. 7, p. 2707-2719 | Abstract: | The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images. | Keywords: | Feature extraction Minimum spanning superpixel tree (MSST) Retinal image Superpixel Vessel segmentation |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on cybernetics | ISSN: | 2168-2267 | EISSN: | 2168-2275 | DOI: | 10.1109/TCYB.2018.2833963 | Rights: | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication B. Sheng et al., "Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector," in IEEE Transactions on Cybernetics, vol. 49, no. 7, pp. 2707-2719, July 2019 is available at https://doi.org/10.1109/TCYB.2018.2833963. |
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