Farkli modalitelerdeki hacimsel medikal goruntulerin otomatik cakistirilmasi

As an important part of clinical knowledge, biomedical images have critical importance for researches and healthcare. With the advances in medical imaging technologies, multimodal images are playing a more and more important role in improving the quality and efficiency of healthcare. Multimodal biomedical volume image registration is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research.Registration by maximization of normalized mutual information is an intensity based approach to register images, which is derived from principles of information theory. Because of its robustness and accuracy, mutual information has become common in intensity based multimodal volume registrations. However, the approach involves time consuming intensitive computations to perform geometrical transformations of a volume image and estimate mutual information between two images.Based on the theory of mutual information, a fully automated, robust and fast registration method was developed in this study, which has high level accuracy in registering various sets of multimodal volume images of brain.In this thesis, a novel method is introduced, which is named as 3-Slice Method, to reduce the computational burden of the geometrical transformations and normalized mutual information (NMI) cost function.Proposed volume image registration method optimizes normalized mutual information measure using a very fast algorithm known as simultaneous perturbation stochastic approximation (SPSA) algorithm. SPSA is applied first time by this study in registering 3-D biomedical volume images. Proposed method uses only three orthogonal central slices of the volumes to build joint histogram instead of entire volumes. Performance results show our method provides remarkable speed improvements over the methods using conventional mutual information computations and other optimization algorithms. In addition to these, an approach is suggested to calculate the SPSA gain parameters automatically, instead of experimental pre-registration trials.