MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION

Abstract

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces.