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

2026-05-20

AI Summary

This study addresses the challenge of discontinuous vessel responses in retinal vascular segmentation, where traditional filters such as Frangi’s often produce fragmented vessels, thereby compromising accurate extraction of vascular structures across multimodal medical images. To overcome this limitation, the authors propose LS-CF, an unsupervised post-processing method that, for the first time, integrates local sensitivity–aware connectivity constraints with a heuristic tolerance mechanism to evaluate and reconnect broken vessel segments at the pixel level—without requiring any training data, thus enabling generalization across diverse imaging modalities. Built upon Frangi responses, LS-CF combines local connectivity analysis with morphological operations to form a lightweight yet effective unsupervised filter. The method achieves state-of-the-art performance on multiple benchmark datasets—including OSIRIX, IOSTAR, DRIVE, STARE, and CHASE_DB—with particularly notable gains over existing unsupervised approaches on CHASE_DB.

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.

Problem

• vessel segmentation
• Frangi filter
• vessel discontinuities
• multimodal imaging
• retinal vessel analysis

Innovation

• local-sensitive connectivity filter
• unsupervised vessel segmentation
• Frangi filter enhancement
• multimodal medical imaging
• vessel continuity heuristic