Predictive modeling of anatomic structures using canonical correlation analysis

Tianming Liu, Dinggang Shen, Christos Davatzikos

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

Abstract

In this paper, we present a method for predictive modeling of anatomic structures using canonical correlation analysis (CCA). Using this technique, certain anatomical structures, such as tumor-distorted structures, can be estimated from others by exploring the correlation between them, which has been determined from a set of training samples. Cortical surfaces and corpus callosum boundaries have been used to demonstrate the performance of the proposed method in predictive modeling. Applications of this method are in estimating brain tissues obscured by tumors and surrounding edema, in detecting abnormal structures, and in formulating alternate forms of statistically-based interpolation and regularization.

Original languageEnglish (US)
Title of host publication2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationMacro to Nano
Pages1279-1282
Number of pages4
StatePublished - Dec 1 2004
Event2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano - Arlington, VA, United States
Duration: Apr 15 2004Apr 18 2004

Publication series

Name2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Volume2

Other

Other2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
CountryUnited States
CityArlington, VA
Period4/15/044/18/04

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Liu, T., Shen, D., & Davatzikos, C. (2004). Predictive modeling of anatomic structures using canonical correlation analysis. In 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (pp. 1279-1282). (2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano; Vol. 2).