Morphological classification of medical images using nonlinear support vector machines

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

Morphological classification of medical images using nonlinear support vector machines

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

Academic Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	Macro to Nano
Pages	587-590
Number of pages	4
Volume	1
State	Published - Dec 1 2004
Event	2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano - Arlington, VA, United States Duration: Apr 15 2004 → Apr 18 2004

Other

Other	2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Country/Territory	United States
City	Arlington, VA
Period	4/15/04 → 4/18/04

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Morphological classification of medical images using nonlinear support vector machines",

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.",

author = "Christos Davatzikos and Dinggang Shen and Zhiqiang Lao and Zhong Xue and Bilge Karacali",

year = "2004",

month = dec,

day = "1",

language = "English (US)",

isbn = "0780383885",

volume = "1",

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note = "2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano ; Conference date: 15-04-2004 Through 18-04-2004",

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T1 - Morphological classification of medical images using nonlinear support vector machines

AU - Davatzikos, Christos

AU - Shen, Dinggang

AU - Lao, Zhiqiang

AU - Xue, Zhong

AU - Karacali, Bilge

PY - 2004/12/1

Y1 - 2004/12/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/17144394821

UR - https://www.scopus.com/inward/citedby.url?scp=17144394821&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:17144394821

SN - 0780383885

SN - 9780780383883

VL - 1

SP - 587

EP - 590

BT - 2004 2nd IEEE International Symposium on Biomedical Imaging

T2 - 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano

Y2 - 15 April 2004 through 18 April 2004

ER -

Morphological classification of medical images using nonlinear support vector machines

Abstract

Other

ASJC Scopus subject areas

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