Sazak, Ç., Nelson, C. J. and Obara, B. (2019) The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition, 88, pp. 739-750. (doi: 10.1016/j.patcog.2018.10.011)
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Abstract
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.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Nelson, Dr Chas |
| Authors: | Sazak, Ç., Nelson, C. J., and Obara, B. |
| College/School: | College of Science and Engineering > School of Physics and Astronomy |
| Journal Name: | Pattern Recognition |
| Publisher: | Elsevier |
| ISSN: | 0031-3203 |
| ISSN (Online): | 1873-5142 |
| Published Online: | 10 October 2018 |
| Copyright Holders: | Copyright © 2019 The Authors |
| First Published: | First published in Pattern Recognition 88: 739-750 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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