Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

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

3D Segmentation of Soft Tissues by Flipping-free Mesh Deformation

Ph.DDOCTOR OF PHILOSOPH

A New Information-Theoretic Measure to Control the Robustness-Sensitivity Trade-Off for DMFFD Point-Set Registration

Abstract. An essential component of many medical image analysis pro-tocols is the establishment and manipulation of feature correspondences. These image features can assume such forms spanning the range of functions of individual or regional pixel intensities to geometric struc-tures extracted as a preprocessing segmentation step. Many algorithms focusing on the latter set of salient features attempt to reduce these structures to such geometric primitives as surfaces, curves and/or points for correspondence-based study. Although the latter geometric primi-tive forms the basis of many of these algorithms, unrealistic constraints such as assumptions of identical cardinality between point-sets hinder general usage. Furthermore, the local structure for certain point-sets de-rived from segmentation processes is often ignored. In this paper, we introduce a family of novel information-theoretic measures for pooint-set registration derived as a generalization of the well-known Shannon entropy known as the Havrda-Charvat-Tsallis entropy. This divergence measure permits a fine-tuning between robustness and sensitivity em-phasis. In addition, we employ a directly manipulated free-form defor-mation (DMFFD) transformation model, a recently developed variant of the well-known FFD transformation model.