Mapping functions driven robust retinal vessel segmentation via training patches

Xia, Haiying, Jiang, Frank, Deng, Shuaifei, Xin, Jing and Doss, Robin 2018, Mapping functions driven robust retinal vessel segmentation via training patches, IEEE access, vol. 6, pp. 61973-61982, doi: 10.1109/ACCESS.2018.2869858.

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Title Mapping functions driven robust retinal vessel segmentation via training patches
Author(s) Xia, Haiying
Jiang, Frank
Deng, Shuaifei
Xin, Jing
Doss, RobinORCID iD for Doss, Robin orcid.org/0000-0001-6143-6850
Journal name IEEE access
Volume number 6
Start page 61973
End page 61982
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2018-09-24
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Fundus images
mapping functions
vessel segmentation
BLOOD-VESSELS
IMAGES
CLASSIFICATION
GABOR
Summary © 2018 IEEE. Vein occlusions and diabetic retinopathy are two of many retinal pathologies affecting the retina. Understanding robust vessel segmentation of fundus images is of vital importance for improving the diagnosis results of these diseases. This paper proposes a novel approach for computing the minimum distance for each test patch via the distance comparison within the test patch and cluster centers. The numerous patches are calculated using manual segmentations through the K-means algorithm. We demonstrate the efficiency of learning the simple pattern from each cluster; meanwhile, the mapping function for each cluster is determined by the patches in the training images and their corresponding manual segmentation patches. Two publicly recognized benchmark data sets, namely DRIVE and STARE, are used in our experimental validation. Experimental results show that the proposed approach outperforms conventional methods for vessel segmentation problems validated via public benchmark data sets, i.e., DRIVE and STARE.
Language eng
DOI 10.1109/ACCESS.2018.2869858
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30117119

Document type: Journal Article
Collections: School of Information Technology
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