Title
Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector.
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.
Year
DOI
Venue
2019
10.1109/TCYB.2018.2833963
IEEE transactions on cybernetics
Keywords
Field
DocType
Image segmentation,Retinal vessels,Detectors,Feature extraction,Image color analysis,Diabetes
Vessel segmentation,Pattern recognition,Segmentation,Image segmentation,Feature extraction,Pixel,Artificial intelligence,Retinal,Detector,Machine learning,Grid,Mathematics
Journal
Volume
Issue
ISSN
49
7
2168-2275
Citations 
PageRank 
References 
11
0.53
19
Authors
9
Name
Order
Citations
PageRank
Bin Sheng136861.19
Ping Li220240.76
Shuangjia Mo3110.53
Huating Li4225.14
Xuhong Hou5474.03
Qiang Wu6132.91
Jing Qin713214.27
Ruogu Fang828721.78
David Dagan Feng93329413.76