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 bowlerhat 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.
Recently, in REF , a multiscale bowler-hat transform method based on mathematical morphology for vessel enhancement is proposed.
The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images
ArXiv
ArXiv
Mathematics Computer Science