Kernel-based manifold learning for statistical analysis of diffusion tensor images

Parmeshwar Khurd, Ragini Verma, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


Diffusion tensor imaging (DTI) is an important modality to study white matter structure in brain images and voxel-based group-wise statistical analysis of DTI is an integral component in most biomedical applications of DTI. Voxel-based DTI analysis should ideally satisfy two desiderata: (1) it should obtain a good characterization of the statistical distribution of the tensors under consideration at a given voxel, which typically lie on a non-linear submanifold of R6, and (2) it should find an optimal way to identify statistical differences between two groups of tensor measurements, e.g., as in comparative studies between normal and diseased populations. In this paper, extending previous work on the application of manifold learning techniques to DTI, we shall present a kernel-based approach to voxel-wise statistical analysis of DTI data that satisfies both these desiderata. Using both simulated and real data, we shall show that kernel principal component analysis (kPCA) can effectively learn the probability density of the tensors under consideration and that kernel Fisher discriminant analysis (kFDA) can find good features that can optimally discriminate between groups. We shall also present results from an application of kFDA to a DTI dataset obtained as part of a clinical study of schizophrenia.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical lmaging - 20th International Conference, IPMI 2007, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)3540732721, 9783540732723
StatePublished - 2007
Externally publishedYes
Event20th International Conference on Information Processing in Medical lmaging, IPMI 2007 - Kerkrade, Netherlands
Duration: Jul 2 2007Jul 6 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other20th International Conference on Information Processing in Medical lmaging, IPMI 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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