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

Abstract

Image registration is a process of creating correspondence between a pair of images. In some situations, the physical oneto-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.

Structure matching driven by joint-saliency-structure adaptive kernel regression

“…To solve the outlier problem, we proposed the joint saliency map (JSM) to group the corresponding saliency structures (called Joint Saliency Structures, JSSs) in intensity-based similarity measure computation [31]. The JSM has been proved to greatly improve the accuracy and robustness of rigid [31] [32] and nonrigid [10] [33][34] image registration with outliers. We further think, by reflecting the local structure correspondence, the JSM also could guide the local kernel regression for accu-rately estimating registration transformations in the nonrigid image registration with outliers.…”