Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering

TL;DR

This paper introduces a cortical-inspired spectral clustering method utilizing a Fokker-Planck-based connectivity kernel to improve vessel connectivity analysis in retinal images, aiding early diagnosis of eye diseases.

Contribution

It presents a novel spectral clustering approach based on visual cortex geometry and Fokker-Planck equations for better vessel connectivity analysis in retinal images.

Findings

Successful vessel identification in hierarchical topology

Effective handling of junctions and crossings

Improved analysis of vessel features

Abstract

Retinal images provide early signs of diabetic retinopathy, glaucoma, and hypertension. These signs can be investigated based on microaneurysms or smaller vessels. The diagnostic biomarkers are the change of vessel widths and angles especially at junctions, which are investigated using the vessel segmentation or tracking. Vessel paths may also be interrupted; crossings and bifurcations may be disconnected. This paper addresses a novel contextual method based on the geometry of the primary visual cortex (V1) to study these difficulties. We have analyzed the specific problems at junctions with a connectivity kernel obtained as the fundamental solution of the Fokker-Planck equation, which is usually used to represent the geometrical structure of multi-orientation cortical connectivity. Using the spectral clustering on a large local affinity matrix constructed by both the connectivity kernel and the feature of intensity, the vessels are identified successfully in a hierarchical topology each representing an individual perceptual unit.