NDLI: Diagnosis of Brain Abnormality Using both Structural and Functional MR Images

Content Provider	IEEE Xplore Digital Library
Author	Yong Fan Rao, H. Giannetta, J. Hurt, H. Jiongjiong Wang Davatzikos, C. Dinggang Shen
Copyright Year	2006
Description	Author affiliation: Dept. of Radiol., Pennsylvania Univ., Philadelphia, PA (Yong Fan)
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
Sponsorship	IEEE EMB
Starting Page	1044
Ending Page	1047
File Size	233126
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Brain Image analysis Support vector machines Support vector machine classification Diseases Pediatrics Machine learning Principal component analysis Image classification
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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