COMPARE: Classification of morphological patterns using adaptive regional elements

  • Fan Y
  • Shen D
  • Gur R
 et al. 
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Abstract

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-pre- serving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Re- gions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smooth- ness of the correlation mapwhich 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 onMRbrain 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.

Author-supplied keywords

  • Feature selection
  • Morphological pattern analysis
  • Pattern classification
  • Regional feature extraction
  • Schizophrenia
  • Structural MRI
  • Support vector machines (SVM)

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