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Lookup NU author(s): Professor Boguslaw ObaraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018 Elsevier Ltd. Enhancement, followed by segmentation, quantification and modelling of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method a new approach based on mathematical morphology for vessel enhancement. The proposed method combines different structuring elements to detect innate features of vessel-like structures. We evaluate the proposed method qualitatively and quantitatively and compare it with the state-of-the-art methods using both synthetic and real datasets. Our results establish that the proposed method achieves high-quality vessel-like structure enhancement in both synthetic examples and clinically relevant retinal images. The bowler-hat transform is shown to be able to detect fine vessels while still remaining robust at junctions.
Author(s): Sazak C, Nelson CJ, Obara B
Publication type: Article
Publication status: Published
Journal: Pattern Recognition
Year: 2019
Volume: 88
Pages: 739-750
Print publication date: 01/04/2019
Online publication date: 10/10/2018
Acceptance date: 09/10/2018
Date deposited: 29/04/2021
ISSN (print): 0031-3203
ISSN (electronic): 1873-5142
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.patcog.2018.10.011
DOI: 10.1016/j.patcog.2018.10.011
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