COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements

• Published in: IEEE transactions on medical imaging Vol. 26; no. 1; pp. 93 - 105
• Main Authors: Yong Fan, Dinggang Shen, Gur, R.C., Gur, R.E., Davatzikos, C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomy; Artificial Intelligence; Brain - pathology; Density measurement; Displays; Feature extraction; Feature selection; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Learning systems; Magnetic resonance; Magnetic Resonance Imaging - methods; morphological pattern analysis; pattern classification; Pattern Recognition, Automated - methods; regional feature extraction; Reproducibility of Results; Robustness; schizophrenia; Schizophrenia - pathology; Sensitivity and Specificity; Software; structural MRI; Studies; Support vector machine classification; Support vector machines; support vector machines (SVM); Volume measurement
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.886812

This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used