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 journalArticlepeer-review


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

ASJC Scopus subject areas

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


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