Morphological classification of medical images using nonlinear support vector machines

Christos Davatzikos, Dinggang Shen, Zhiqiang Lao, Zhong Xue, Bilge Karacali

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

3 Scopus citations

Abstract

The wavelet decomposition of a high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine pattern classification method to the morphological signatures. By considering measurements from the entire image, and not only from isolated anatomical structures, and by using a highly non-linear classifier, this method has achieved very high classification results in a variety of tests.

Original languageEnglish (US)
Title of host publication2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationMacro to Nano
Pages587-590
Number of pages4
Volume1
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

Other

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

ASJC Scopus subject areas

  • Engineering(all)

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