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

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• 作者：Pamela Guevara ; Cyril Poupon ; Denis Rivi¨¨re ; Yann Cointepas ; Linda Marrakchi ; et al.
• 刊名：Lecture Notes in Computer Science
• 出版年：2010
• 出版时间：2010
• 年：2010
• 卷：6361
• 期：1
• 页码：550-557
• 全文大小：577.9 KB

文摘

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 multiresolution 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.