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

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• 作者：Rashmi Panda ; ^{rp14@iitbbs.ac.in" class="auth_mail" title="E-mail the corresponding author} ; N.B. Puhan ^{nbpuhan@iitbbs.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Ganapati Panda ^{gpanda@iitbbs.ac.in" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Vessel segmentation ; Glaucoma ; Diabetic retinopathy ; Seeded region growing ; Symmetry ; Hausdorff distance
• 刊名：Biocybernetics and Biomedical Engineering
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：36
• 期：1
• 页码：119-129
• 全文大小：1724 K

文摘

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).