Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering
This addresses vessel connectivity issues in retinal image analysis for medical diagnosis, but appears incremental as it builds on existing spectral clustering and cortical models.
The paper tackled the problem of analyzing vessel connectivities in retinal images for diagnosing diseases like diabetic retinopathy, by proposing a novel contextual method based on the geometry of the primary visual cortex to identify vessels in a hierarchical topology.
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.