Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IEMBS.2006.259260 | 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 |
Keywords | DocType | Volume |
image registration,statistical analysis,image classification,image analysis,function representation,support vector machines,young adult,machine learning,neurophysiology,learning artificial intelligence,feature selection,leave one out cross validation,brain imaging,principal component analysis,support vector machine,feature extraction,pediatrics | Conference | Suppl |
ISSN | Citations | PageRank |
1557-170X | 6 | 0.68 |
References | Authors | |
13 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Fan | 1 | 508 | 29.13 |
| Hengyi Rao | 2 | 96 | 9.41 |
| Joan Giannetta | 3 | 46 | 2.58 |
| Hallam Hurt | 4 | 46 | 3.59 |
| Jiongjiong Wang | 5 | 140 | 12.13 |
| Christos Davatzikos | 6 | 3865 | 335.91 |
| Dinggang Shen | 7 | 7837 | 611.27 |