A Topology Preserving Non-Rigid Registration Algorithm with Integration Shape Knowledge to Segment Brain Subcortical Structures from MRI Images
Résumé
A new non-rigid registration method combining image intensity and a priori shape knowledge of the objects in the image is proposed. This method, based on optical flow theory, uses a topology correction strategy to prevent topological changes of the deformed objects and the a priori shape knowledge to keep the object shapes during the deformation process. Advantages of the method over classical intensity based non-rigid registration are that it can improve the registration precision with the a priori knowledge and allows to segment objects at the same time, especially efficient in the case of segmenting adjacent objects having similar intensities. The proposed algorithm is applied to segment brain subcortical structures from 15 real brain MRI images and evaluated by comparing with ground truths. The obtained results show the efficiency and robustness of our method.