Research On Multi-Modality Medical Image Registration

With the rapid development of imaging technology, there are more and more different modality medical images in application fields. Integrating those images is helpful to improve the accuracy of clinical diagnoses and surgical therapies .Image registration is the key part of integration. It's a geometrical transformation aligning two images according to the corresponding features.This thesis introduces several geometrical transformations, optimization methods, similarity measures. According to whether extracting images' feature or not , we divide present methods into two parts, feature extraction based (FEB) methods and voxel similarity based (VSB) methods.Maximum mutual information(MMI) is the most popular method in VSB. In this thesis we analyse its characters. It shows high performance in case of adding different noise to the floating image. When two images have different resolutions, the higher resolution the images have, the better result it turns out ,but it will consumes much more time. We also focus on partial volume (PV) interpolation algorithm .The value of mutual information(MI) doesn't increase or decline in one direction at translation from one integer to the nearest integer. MMI doesn't consider the spatial information, we introduce two other MI based methods: derivation of MI and high-order MI.MMI computes the overlap area of two images. The changing of the overlap with different fields of view leads to the value of each entry in MI changed. MMI doesn't consider the relations about these changes. It shows misalignment. In this thesis we analyse this situation. Then we normalized the mutual information, which testified an overlap invariant measure, so we use normalized mutual information(NMI) as a new similarity measure. After theoretical and experimental analyses we draw a conclusion it's a better method than MMI in multi-modality image registration.