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

arXiv: https://arxiv.org/abs/1709.05495 · HTML: https://mlsanity.com/paper/1709.05495/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images](https://mlsanity.com/paper/2112.09493/) (2021)
- [EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation](https://mlsanity.com/paper/2107.01351/) (2021)
- [A Review of Modern Approaches for Coronary Angiography Imaging Analysis](https://mlsanity.com/paper/2209.13997/) (2022)
- [Progressive Transfer Learning for Multi-Pass Fundus Image Restoration](https://mlsanity.com/paper/2504.10025/) (2025)

## Abstract (arXiv)

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.