Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM

Authors:
Yong Fan;Dinggang Shen;Christos Davatzikos
Affiliations:
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA;Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA;Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.