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

Sazak, Cigdem; Nelson, Carl J.; Obara, Boguslaw

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.

Citation

Sazak, C., Nelson, C. J., & Obara, B. (2019). The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition, 88, 739-750. https://doi.org/10.1016/j.patcog.2018.10.011

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2018
• Online Publication Date: Oct 10, 2018
• Publication Date: Apr 30, 2019
• Deposit Date: Oct 9, 2018
• Publicly Available Date: Oct 10, 2019
• Journal: Pattern Recognition
• Print ISSN: 0031-3203
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 88
• Pages: 739-750
• DOI: https://doi.org/10.1016/j.patcog.2018.10.011
• Public URL: https://durham-repository.worktribe.com/output/1316871