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Segmentation of Thalamic Nuclei from DTI Using Spectral Clustering

  • Ulas Ziyan
  • David Tuch
  • Carl-Fredrik Westin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

Recent work shows that diffusion tensor imaging (DTI) can help resolving thalamic nuclei based on the characteristic fiber orientation of the corticothalamic/thalamocortical striations within each nucleus. In this paper we describe a novel segmentation method based on spectral clustering. We use Markovian relaxation to handle spatial information in a natural way, and we explicitly minimize the normalized cut criteria of the spectral clustering for a better optimization. Using this modified spectral clustering algorithm, we can resolve the organization of the thalamic nuclei into groups and subgroups solely based on the voxel affinity matrix, avoiding the need for explicitly defined cluster centers. The identification of nuclear subdivisions can facilitate localization of functional activation and pathology to individual nuclear subgroups.

Keywords

Thalamic Nucleus Spectral Cluster Spectral Cluster Algorithm Tensor Volume Expert Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ulas Ziyan
    • 1
  • David Tuch
    • 2
  • Carl-Fredrik Westin
    • 1
    • 3
  1. 1.MIT Computer Science and Artificial Intelligence LabCambridgeUSA
  2. 2.Novartis Pharma AGBaselSwitzerland
  3. 3.Laboratory of Mathematics in ImagingBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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