Multivariate hypothesis testing of DTI data for tissue clustering

Raisa Z. Freidlin, Yaniv Assaf, Peter J. Basser

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work we investigate the feasibility and effectiveness of unsupervised tissue clustering and classification algorithms for DTI data. Tissue clustering and classification are among the most challenging tasks in DT image analysis. While clustering separates acquired data into objects, tissue classification provides in-depth information about each region of interest. The unsupervised clustering algorithm utilizes a framework proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors arising from the same distribution is determined by an F-test. Tissue type is classified according to one of three possible diffusion models (general anisotropic, prolate, or oblate), which is determined with a parsimonious model selection framework. This approach, also adapted from Snedecor, chooses among different models of diffusion within a voxel using a series of F-tests. Both numerical phantoms and DWI data obtained from excised rat spinal cord are used to test and validate these tissue clustering and classification approaches.

Original languageEnglish
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages776-779
Number of pages4
DOIs
StatePublished - 2007
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: 12 Apr 200715 Apr 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Conference

Conference2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA
Period12/04/0715/04/07

Keywords

  • Clustering
  • DTI
  • Diffusion tensor
  • Hypothesis testing
  • Parsimonious

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