Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation

TL;DR

The paper introduces LS-CF, an unsupervised post-processing filter that enhances multimodal vessel segmentation by improving vessel continuity detection over existing methods.

Contribution

It proposes a novel local-sensitive connectivity filter that improves vessel segmentation accuracy without supervision, outperforming current state-of-the-art approaches.

Findings

Achieved superior accuracy on OSIRIX angiographic dataset.

Outperformed all unsupervised methods on CHASE-DB dataset.

Demonstrated robustness across multiple multimodal datasets.

Abstract

A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.