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.
Highlights
Many biomedical images contain vessel-like structures, such as blood vessels or cytoskeletal networks [1]
We evaluate these results in a quantitative and comparable manner using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric
We introduce a new enhancement method for vessel-like structures based on mathematical morphology, which exploits the elongated shape of vessel-like structures
Summary
Many biomedical images contain vessel-like structures, such as blood vessels or cytoskeletal networks [1] Automated extraction of these structures and their connected network is often an essential step in quantitative image analysis and computer-aided diagnostic pipelines. A wide range of vessel enhancement methods have been proposed (see [2] and [1] for a recent review) These include Hessian [3,4,5], Phase Congruency Tensor [6,7], mathematical morphology [5,8,9], adaptive histogram equalisation [10] based approaches and many others [11,12,13,14,15,16,17,18]
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