The Multiscale Bowler-Hat Transform for Vessel Enhancement in 3D Biomedical Images
This work addresses vessel enhancement for biomedical imaging applications, but it appears incremental as it builds on mathematical morphology with a novel combination of structuring elements.
The paper tackles the problem of enhancing 3D vessel-like structures in biomedical images, which existing methods often fail to handle uniformly across different radii and at junctions, and proposes a 3D bowler-hat transform based on mathematical morphology that achieves high-quality enhancement in synthetic and real data, showing robustness to thickness variations and junctions.
Enhancement and detection of 3D vessel-like structures has long been an open problem as most existing image processing methods fail in many aspects, including a lack of uniform enhancement between vessels of different radii and a lack of enhancement at the junctions. Here, we propose a method based on mathematical morphology to enhance 3D vessel-like structures in biomedical images. The proposed method, 3D bowler-hat transform, combines sphere and line structuring elements to enhance vessel-like structures. The proposed method is validated on synthetic and real data and compared with state-of-the-art methods. Our results show that the proposed method achieves a high-quality vessel-like structures enhancement in both synthetic and real biomedical images, and is able to cope with variations in vessels thickness throughout vascular networks while remaining robust at junctions.