Automatic Correspondence on Medical Images: A Comparative Study of Four Methods for Allocating Corresponding Points

Abstract

The accurate estimation of point correspondences is often required in a wide variety of medical imageprocessing applications. Numerous point correspondence methods have been proposed in this field, each exhibiting its own characteristics, strengths, and weaknesses. This paper presents a comprehensive comparison of four automatic methods for allocating corresponding points, namely the template-matching technique, the iterative closest points approach, the correspondence by sensitivity to movement scheme, and the self-organizing maps algorithm. Initially, the four correspondence methods are described focusing on their distinct characteristics and their parameter selection for common comparisons. The performance of the four methods is then qualitatively and quantitatively compared over a total of 132 two-dimensional image pairs divided into eight sets. The sets comprise of pairs of images obtained using controlled geometry protocols (affine and sinusoidal transforms) and pairs of images subject to unknown transformations. The four methods are statistically evaluated pairwise on all image pairs and individually in terms of specific features of merit based on the correspondence accuracy as well as the registration accuracy. After assessing these evaluation criteria for each method, it was deduced that the self-organizing maps approach outperformed in most cases the other three methods in comparison.

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“…To find robust point correspondence, some approaches are proposed including soft correspondence detections [ 20 , 35 ], joint clustering-matching strategy [ 36 ] and modeling point sets by kernel density function [ 37 ]. Compared with the classical template matching, the iterative closest point [ 38 ] and the correspondence by sensitivity to movement [ 39 ], the self-organizing map [ 40 ] algorithm was considered in [ 41 ] to be the most effective method in 2D feature point correspondence detection. However, these methods do not consider the complexity of correspondence detection in the context of local large structure distortion combined with the outliers.…”