부산대학교 도서관

In this paper a hierarchical brain segmentation from multiple MRIs is presented for a global-to-local shape analysis. The idea is to group voxels into clusters with high within-cluster and low between-cluster shape relations. Doing so, complementing voxels are analysed together, optimally wheeling the power of multivariate analysis. Therefore, we adapted hierarchical spectral clustering to volumetric image datasets. The outcome is a segmentation of the brain into regions at different levels of detail and anatomical overlap, which are tested in their ability to predict ADAS-cog (a score for cognitive function in Alzheimer's disease (AD) diagnosis) from brain shape. The results show a benefit for a global-to-local analysis compared to a typical voxel-based and whole brain-based analysis. Additionally, knowledge on brain variations in AD is perfectly confirmed and even elaborated on.