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The multiscale bowler-hat transform for blood vessel enhancement in retinal images

Lookup NU author(s): Professor Boguslaw Obara

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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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