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This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles.
►We propose a robust fiber clustering method inferring the main fiber bundles present in any diffusion-based tract dataset. ►The method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. ►Is the first method capable to handle a very big number of fibers. ►Results can be used for the inference of a brain fiber bundle model from an intersubject analysis.