Diagnosis of brain abnormality using both structural and functional MR images.

Yong Fan, Hengyi Rao, Joan Giannetta, Hallam Hurt, Jiongjiong Wang, Christos Davatzikos, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual brain in a template space, within a mass preserving framework. Then, statistical regional features are extracted to reduce the dimensionality of morphological and functional representations, as well as to achieve the robustness to registration errors and inter-subject variations. Finally, the most discriminative regional features are selected by a hybrid feature selection method for brain classification, using a nonlinear support vector machine. The proposed method has been applied to classifying the brain images of prenatally cocaine-exposed young adults from those of socioeconomically matched controls, resulting in 91.8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification.

Original languageEnglish (US)
Pages (from-to)1044-1047
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2006
Externally publishedYes

Fingerprint

Brain
Brain Diseases
Cocaine
Young Adult
Support vector machines
Learning systems
Feature extraction

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Diagnosis of brain abnormality using both structural and functional MR images. / Fan, Yong; Rao, Hengyi; Giannetta, Joan; Hurt, Hallam; Wang, Jiongjiong; Davatzikos, Christos; Shen, Dinggang.

In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2006, p. 1044-1047.

Research output: Contribution to journalArticle

@article{8fc5f688f6af4df3bb565b70fd670161,
title = "Diagnosis of brain abnormality using both structural and functional MR images.",
abstract = "A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual brain in a template space, within a mass preserving framework. Then, statistical regional features are extracted to reduce the dimensionality of morphological and functional representations, as well as to achieve the robustness to registration errors and inter-subject variations. Finally, the most discriminative regional features are selected by a hybrid feature selection method for brain classification, using a nonlinear support vector machine. The proposed method has been applied to classifying the brain images of prenatally cocaine-exposed young adults from those of socioeconomically matched controls, resulting in 91.8{\%} correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification.",
author = "Yong Fan and Hengyi Rao and Joan Giannetta and Hallam Hurt and Jiongjiong Wang and Christos Davatzikos and Dinggang Shen",
year = "2006",
language = "English (US)",
pages = "1044--1047",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Diagnosis of brain abnormality using both structural and functional MR images.

AU - Fan, Yong

AU - Rao, Hengyi

AU - Giannetta, Joan

AU - Hurt, Hallam

AU - Wang, Jiongjiong

AU - Davatzikos, Christos

AU - Shen, Dinggang

PY - 2006

Y1 - 2006

N2 - A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual brain in a template space, within a mass preserving framework. Then, statistical regional features are extracted to reduce the dimensionality of morphological and functional representations, as well as to achieve the robustness to registration errors and inter-subject variations. Finally, the most discriminative regional features are selected by a hybrid feature selection method for brain classification, using a nonlinear support vector machine. The proposed method has been applied to classifying the brain images of prenatally cocaine-exposed young adults from those of socioeconomically matched controls, resulting in 91.8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification.

AB - A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual brain in a template space, within a mass preserving framework. Then, statistical regional features are extracted to reduce the dimensionality of morphological and functional representations, as well as to achieve the robustness to registration errors and inter-subject variations. Finally, the most discriminative regional features are selected by a hybrid feature selection method for brain classification, using a nonlinear support vector machine. The proposed method has been applied to classifying the brain images of prenatally cocaine-exposed young adults from those of socioeconomically matched controls, resulting in 91.8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification.

UR - http://www.scopus.com/inward/record.url?scp=84903861278&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903861278&partnerID=8YFLogxK

M3 - Article

SP - 1044

EP - 1047

JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

SN - 1557-170X

ER -