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This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM), is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case.

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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.