Inference of a HARDI Fiber Bundle Atlas Using a Two-Level Clustering Strategy

Abstract. This paper presents a method inferring a model of the brain white matter organisation from HARDI tractography results computed for a group of subjects. This model is made up of a set of generic fiber bundles that can be detected in most of the population. Our approach is based on a two-level clustering strategy. The first level is a multires-olution intra-subject clustering of the million tracts that are computed for each brain. This analysis reduces the complexity of the data to a few thousands fiber bundles for each subject. The second level is an inter-subject clustering over fiber bundle centroids from all the subjects using a pairwise distance computed after spatial normalization. The resulting model includes the large bundles of anatomical literature and about 20 U-fiber bundles in each hemisphere.