The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images
This is an incremental improvement for medical imaging in ophthalmology, enhancing vessel detection in retinal images.
The paper tackled the problem of enhancing blood vessels in retinal images for computer-aided retinopathy diagnosis by introducing a new bowler-hat transform based on mathematical morphology, achieving high-quality enhancement that detects fine vessels and remains robust at junctions.
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.