Brain Magnetic Resonance (MR) imaging is widely used in clinical practice for disease diagnosis, patient follow-up, therapy evaluation and human brain mapping. In order to extract useful information from MR images, image registration and image segmentation are two crucial procedures in practice. Image registration is necessary in order to compare or combine information obtained from different images. Image segmentation is commonly used to extract more meaningful representation of an image for analysis. In medical image analysis, the two processes are not independent but closely related to each other. Due to some image artifacts introduced in the imaging stage, automatic segmentation relying on the segmenting image alone is still challenging for brain MR images. Therefore, registration-...[ Read more ]

Brain Magnetic Resonance (MR) imaging is widely used in clinical practice for disease diagnosis, patient follow-up, therapy evaluation and human brain mapping. In order to extract useful information from MR images, image registration and image segmentation are two crucial procedures in practice. Image registration is necessary in order to compare or combine information obtained from different images. Image segmentation is commonly used to extract more meaningful representation of an image for analysis. In medical image analysis, the two processes are not independent but closely related to each other. Due to some image artifacts introduced in the imaging stage, automatic segmentation relying on the segmenting image alone is still challenging for brain MR images. Therefore, registration-based segmentation is essential and commonly applied for simplifying the segmentation task. In this thesis, we make contributions in both image registration and registration-based segmentation areas.

In the first part, we propose a novel image registration method derived from a physics model, i.e., the crystal dislocation model. An analogy is made between the registration process and the dislocation system in physics, and thus an elastic interaction between the reference image and the moving image is derived to drive the registration process. It is proved that the proposed registration method not only can improve the registration accuracy, but also achieve a high convergence rate in the optimization procedure.

In the second part, we focus on improving the performance of registration-based segmentation. In registration-based segmentation methods, the target image is segmented through registering the atlas image to the target image and transforming the atlas tissue labels to the target image. An atlas is defined as the combination of an intensity image and its pre-segmented image. We first propose a new way for atlas construction from a population of subjects. It is proposed to divide the whole population into several subgroups and a newly designed tissue-wise weighted groupwise registration method is implemented to produce atlas for each subgroup. The new atlas construction scheme is evaluated through using the constructed atlas(es) for segmentation. It is experimentally validated that our method outperforms other conventional ways for building the atlas.

The second contribution in registration-based segmentation is that a new concept, i.e., atlas-guided groupwise segmentation, is proposed. Groupwise segmentation uses one single atlas image as guidance to segment a population of target images simultaneously. It is developed based on a Markov Random Field (MRF) deformation model to impose the consistency constraints among the population of target images and to embed the prior shape information of the atlas. The experiment results demonstrate that the proposed groupwise segmentation method can achieve higher accuracy than the state-of-the-art registration-based segmentation methods.