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 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.
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Keywords
- Fiber Bundle
- Spatial Normalization
- Orientation Distribution Function
- Inferior Longitudinal Fasciculus
- Talairach Space
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Guevara, P. et al. (2010). Inference of a HARDI Fiber Bundle Atlas Using a Two-Level Clustering Strategy. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_67
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DOI: https://doi.org/10.1007/978-3-642-15705-9_67
Publisher Name: Springer, Berlin, Heidelberg
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