3-D Deformable Registration Using a Statistical Atlas with Applications in Medicine

Abstract

Registering medical images of different individuals is difficult due to inherent anatomical variabilities and possible pathologies. This thesis focuses on characterizing non-pathological variations in human brain anatomy, and applying such knowledge to achieve accurate 3-D deformable registration. Inherent anatomical variations are automatically extracted by deformably registering training data with an expert-segmented 3-D image, a digital brain atlas. Statistical properties of the density and geometric variations in brain anatomy are measured and encoded into the atlas to build a statistical atlas. These statistics can function as prior knowledge to guide the automatic registration process. Compared to an algorithm with no knowledge guidance, reg-istration using the statistical atlas reduces the overall error on 40 test cases by 34%. Automatic registration between the atlas and a subject?s data adapts the expert segmentation for the subject, thus reduces the months-long manual segmentation process to minutes. Accurate and efficient segmentation of medical images enable quantitative study of anatomical differences between populations, as well as detection of abnormal variations indicative of pathologies.