Curvature Orientation Histograms for Detection and Matching of Vascular Landmarks in Retinal Images

Registration is a primary step in tracking pathological changes in medical images. Point-based registration requires a set of distinct, identifiable and comparable landmark points to be extracted from images. In this work, we illustrate a method for obtaining landmarks based on changes in a topographic descriptor of a retinal image. Building on the curvature primal sketch introduced by Asada and Brady for describing interest points on planar curves, we extend the notion to grayscale images. We view an image as a topographic surface and propose to identify interest points on the surface using the surface curvature as a descriptor. This is illustrated by modeling retinal vessels as trenches and identifying landmarks as points where the trench behaviour changes, such as it splits or bends sharply. Based on this model, we present a method which uses the surface curvature to characterise landmark points on retinal vessels as points of high dispersion in the curvature orientation histogram computed around the points. This approach yields junction/crossover points of retinal vessels and provides a means to derive additional information about the type of junction. A scheme is developed for using such information and determining the correspondence between sets of landmarks from two images related by a rigid transformation. In this paper we present the details of the proposed approach and results of testing on images from public domain datasets. Results include comparison of landmark detection with two other methods, and results of correspondence derivation. Results show the approach to be successful and fast.