3-d deformable registration of medical images using a statistical atlas

Abstract

Registration between 3-D images of human anatomies enables cross-subject diagnosis. However, innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult. We characterize such anatomical variations to achieve accurate registration. We represent anatomical variations in the form of statistical models, and embed these statistics into a 3-D digital brain atlas which we use as a reference. When we register the statistical atlas with a particular subject, the embedded statistics function as prior knowledge to guide the deformation process. This method gives an overall voxel mis-classification rate of 2.9% on 40 test cases; this is a 34% error reduction over the performance of our previous algorithm without using anatomical knowledge.