A Comparison of Metrics and Algorithms for Fiber Clustering

Diffusion-weighted Magnetic Resonance Imaging (dMRI) can unveil the microstructure of the brain white matter. The analysis of the anisotropy observed in the dMRI contrast with tractography methods can help to understand the pattern of connections between brain regions and characterize neurological d...

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Bibliographic Details
Published in2013 International Workshop on Pattern Recognition in Neuroimaging pp. 190 - 193
Main Authors Siless, Viviana, Medina, Sergio, Varoquaux, Gael, Thirion, Bertrand
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Algorithm design and analysis
Clustering algorithms
Computational modeling
Data models
Diffusion tensor imaging
DTI clustering
DWI imaging
fiber clustering
Measurement
point density model
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Summary:Diffusion-weighted Magnetic Resonance Imaging (dMRI) can unveil the microstructure of the brain white matter. The analysis of the anisotropy observed in the dMRI contrast with tractography methods can help to understand the pattern of connections between brain regions and characterize neurological diseases. Because of the amount of information produced by such analyses and the errors carried by the reconstruction step, it is necessary to simplify this output. Clustering algorithms can be used to group samples that are similar according to a given metric. We propose to explore the well-known clustering algorithm k-means and a recently available one, Quick Bundles [1]. We propose an efficient procedure to associate k-means with Point Density Model, a recently proposed metric to analyze geometric structures. We analyze the performance and usability of these algorithms on manually labeled data and a database a 10 subjects.
DOI:10.1109/PRNI.2013.56