Robust adaptive non-rigid image registration based on joint salient 
point sets in the presence of tumor-like gross outliers

Binjie Qin; Zhijun Gu

doi:10.1117/12.755417

Abstract

Image registration is a process of creating correspondence between a pair of images. In some situations, the physical one-to-one correspondence may not exist due to the presence of "outlier" objects (called gross outliers) that appear in one image but not the other. In this paper, a novel robust method is presented to address the problem of tumor-like gross outliers in non-rigid image registration. First, two salient point sets are extracted from the two images to be registered, and classified by means of clustering analysis which is based on Gaussian mixture models and expectation-maximization (EM) algorithm. Then by means of joint saliency map that represents the joint salient regions of the overlapping volume of the two images, the regions including tumor-like gross outliers could be automatically recognized. After screening out of salient points and elimination of outlier points, some stable control points that well represent the corresponding structures within the joint salient regions of the two images could be obtained. By iteratively finding correspondences between the control points in the joint salient regions, the smooth deformation field is approximated based on radial basis functions (RBFs) with compact support until the convergence to the steady-state solution is achieved. Experimental results show that the proposed method is able to recover local deformation caused by tumor resection in brain.

Citation Download Citation

Binjie Qin, Zhijun Gu, "Robust adaptive non-rigid image registration based on joint salient point sets in the presence of tumor-like gross outliers", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 683320 (29 November 2007); doi: 10.1117/12.755417; https://doi.org/10.1117/12.755417

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