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 Citations (Scopus)

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
Title of host publication2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationMacro to Nano
Pages587-590
Number of pages4
Publication statusPublished - 2004
Event2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano - Arlington, VA, United States
Duration: 2004 Apr 152004 Apr 18

Publication series

Name2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Volume1

Other

Other2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
CountryUnited States
CityArlington, VA
Period04/4/1504/4/18

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Davatzikos, C., Shen, D., Lao, Z., Xue, Z., & Karacali, B. (2004). Morphological classification of medical images using nonlinear support vector machines. In 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (pp. 587-590). (2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano; Vol. 1).