The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images

TL;DR

This paper introduces a multiscale bowler-hat transform based on mathematical morphology for enhanced detection of blood vessels in retinal images, improving the visibility of fine vessels and junctions for better diagnosis.

Contribution

It proposes a novel vessel enhancement method that combines multiple structuring elements, outperforming existing techniques in both synthetic and real retinal image datasets.

Findings

High-quality enhancement of vessel-like structures achieved

Effective detection of fine vessels and junctions demonstrated

Outperforms state-of-the-art methods in evaluations

Abstract

Enhancement, followed by segmentation, quantification and modelling, of blood vessels in retinal images plays an essential role in computer-aid retinopathy diagnosis. In this paper, we introduce a new vessel enhancement method which is the bowler-hat transform based on mathematical morphology. 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 existing, state-of-the-art methods using both synthetic and real datasets. Our results show that the proposed method achieves high-quality vessel-like structure enhancement in both synthetic examples and in clinically relevant retinal images, and is shown to be able to detect fine vessels while remaining robust at junctions.