A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus PhotographsShow others and affiliations
2019 (English)In: Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings / [ed] David Riaño; Szymon Wilk; Annette ten Teije, Springer, 2019, p. 347-351Conference paper, Published paper (Refereed)
Abstract [en]
Segmentation of retinal blood vessels is an important diagnostic procedure in ophthalmology. In this paper we propose an automated blood vessels segmentation method that combines both supervised and un-supervised approaches. A novel descriptor named Local Haar Pattern (LHP) is proposed to describe retinal pixel of interest. The performance of the proposed method has been evaluated on three publicly available DRIVE, STARE and CHASE_DB1 datasets. The proposed method achieves an overall segmentation accuracy of 96%, 96% and 95% respectively on DRIVE, STARE, and CHASE DB1 datasets, which are better than the state-of-the-art methods.
Place, publisher, year, edition, pages
Springer, 2019. p. 347-351
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11526
Keywords [en]
Color fundus photographs, Vessel segmentation, Haar feature, Multiscale line detector, Random forest
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Research subject
Computerized Image Analysis; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-96711DOI: 10.1007/978-3-030-21642-9_44ISI: 000495606500044ISBN: 9783030216412 (print)ISBN: 9783030216429 (electronic)OAI: oai:DiVA.org:oru-96711DiVA, id: diva2:1632424
Conference
17th Conference on Artificial Intelligence in Medicine (AIME 2019), Poznan, Poland, June 26–29, 2019
2022-01-262022-01-262022-02-09Bibliographically approved