| Medical image modalities, such as magnetic resonance (MR), positron emission tomography (PET), computed tomography (CT) enable the study of anatomy and function of animals and humans. The combination of images can often lead to additional clinical information not apparent in the separate images. This research seeks to address the issue of non-rigid multi-modality image registration by developing efficient and robust algorithms which are tested by both simulated and real image data sets.; The research consists of two parts: rigid and non-rigid image registration, where rigid registration acts as the first step of non-rigid registration. We proposed a combination of Powell's direction set method (PDSM) with the differential evolution (DE) genetic algorithm for the rigid registration task, which is robust and effective for both synthetic and real image data. Mutual information (MI) allows the algorithm to be efficient for both single- and multi-modality registration task. In addition to the rigid transform, we also developed a fast fluid algorithm using the least mean square (LMS) inverse filtering technique. The test results show that the average CPU speed-up is about 2.5 and the registration accuracy is also improved to some extent (134% for intra-subject and 117% for inter-subject) for the non-rigid mono-modality image registration problem. MI is again chosen as the similarity measure for the non-rigid multi-modality registration in our proposed fast fluid algorithm. A canine brain image data set including both MRI and muCT is built for the validation of the proposed fast fluid algorithm. Moreover, a segmentation-based validation procedure is introduced for the multi-modal registration problem. The results illustrate a speed-up of 2.2 in this case. |
