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Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain

Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain

Junming Shao, Klaus Hahn, Qinli Yang, Afra Wohlschläeger, Christian Boehm, Nicholas Myers, Claudia Plant
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 25
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781613502921|DOI: 10.4018/jkdb.2010100101
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MLA

Shao, Junming, et al. "Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain." IJKDB vol.1, no.4 2010: pp.1-25. http://doi.org/10.4018/jkdb.2010100101

APA

Shao, J., Hahn, K., Yang, Q., Wohlschläeger, A., Boehm, C., Myers, N., & Plant, C. (2010). Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 1(4), 1-25. http://doi.org/10.4018/jkdb.2010100101

Chicago

Shao, Junming, et al. "Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 1, no.4: 1-25. http://doi.org/10.4018/jkdb.2010100101

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Abstract

Diffusion tensor magnetic resonance imaging (DTI) provides a promising way of estimating the neural fiber pathways in the human brain non-invasively via white matter tractography. However, it is difficult to analyze the vast number of resulting tracts quantitatively. Automatic tract clustering would be useful for the neuroscience community, as it can contribute to accurate neurosurgical planning, tract-based analysis, or white matter atlas creation. In this paper, the authors propose a new framework for automatic white matter tract clustering using a hierarchical density-based approach. A novel fiber similarity measure based on dynamic time warping allows for an effective and efficient evaluation of fiber similarity. A lower bounding technique is used to further speed up the computation. Then the algorithm OPTICS is applied, to sort the data into a reachability plot, visualizing the clustering structure of the data. Interactive and automatic clustering algorithms are finally introduced to obtain the clusters. Extensive experiments on synthetic data and real data demonstrate the effectiveness and efficiency of our fiber similarity measure and show that the hierarchical density-based clustering method can group these tracts into meaningful bundles on multiple scales as well as eliminating noisy fibers.

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