University of Oulu

Liu, Qing; Zou, Beiji; Chen, Jie; Chen, Zailiang; Zhu, Chengzhang; Yue, Kejuan; and Zhao, Guoying. Retinal Vessel Segmentation from Simple to Difficult. In: Chen X, Garvin MK, Liu J, Trucco E, Xu Y editors. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016. 57–64. https://doi.org/10.17077/omia.1047

Retinal vessel segmentation from simple to difficult

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Author: Liu, Qing1,2; Zou, Beiji1; Chen, Jie2;
Organizations: 1School of information science and engineering, Central South University
2Center for Machine Vision and Signal Analysis, University of Oulu
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019072923217
Language: English
Published: University of Iowa, 2016
Publish Date: 2019-09-10
Description:

Abstract

In this paper, we propose two vesselness maps and a simple to difficult learning framework for retinal vessel segmentation which is ground truth free. The first vesselness map is the multiscale centrelineboundary contrast map which is inspired by the appearance of vessels. The other is the difference of diffusion map which measures the difference of the diffused image and the original one. Meanwhile, two existing vesselness maps are generated. Totally, 4 vesselness maps are generated. In each vesselness map, pixels with large vesselness values are regarded as positive samples. Pixels around the positive samples with small vesselness values are regarded as negative samples. Then we learn a strong classifier for the retinal image based on other 3 vesselness maps to determine the pixels with mediocre values in single vesselness map. Finally, pixels with two classifier supports are labelled as vessel pixels. The experimental results on DRIVE and STARE show that our method outperforms the state-of-the-art unsupervised methods and achieves competitive performances to supervised methods.

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Series: Iowa research online
ISSN-E: 2476-1680
Pages: 57 - 64
DOI: 10.17077/omia.1047
OADOI: https://oadoi.org/10.17077/omia.1047
Host publication: Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop (OMIA 2016) Held in Conjunction with MICCAI 2016
Conference: International Workshop Ophthalmic Medical Image Analysis
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: Q. Liu was supported by the scholarship from China Scholarship Council. B. Zou and Z. Chen were supported by the NSF of China under Grant No.61573380 and No. 61440055. G. Zhao and J. Chen were supported by Academy of Finland, Tekes Fidipro Program and Infotech Oulu.
Copyright information: Copyright © 2016 the authors.