Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images

TL;DR

This paper introduces a novel 5D kernel-based method inspired by visual cortex geometry to improve the extraction of vessel connections in retinal images, effectively handling crossings, bifurcations, and highly curved vessels.

Contribution

It proposes a new multi-orientation score approach with a 5D cortical connectivity kernel and spectral clustering, enhancing vessel segmentation in complex retinal structures.

Findings

Successfully handles crossings and bifurcations

Outperforms existing methods on challenging structures

Validated on artificial and real retinal images

Abstract

Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems for large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. In this paper, we propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a five-dimensional kernel obtained as the fundamental solution of the Fokker-Planck equation modelling the cortical connectivity in the lifted space of positions, orientations, curvatures and intensity. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy and challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques.