Perelman School of Medicine at the University of Pennsylvania

Section for Biomedical Image Analysis (SBIA)

participating with CBICA

Classification of Morphological Patterns using Aadaptive Regional Elements

COMPARE is a method for classification of structural brain magnetic resonance (MR) images, which is a combination of deformation-based morphometry and machine learning methods. Before running classification, a morphological representation of the anatomy of interest is obtained from structural MR brain images using a high-dimensional mass-preserving template warping method [1, 2]. Regions that display strong correlations between tissue volumes and classification (clinical) variables learned from training samples are extracted using a watershed segmentation algorithm. To achieve robustness to outliers, the regional smoothness of the correlation map is estimated by a cross-validation strategy. A volume increment algorithm is then applied to these regions to extract regional volumetric features. To improve efficiency and generalization ability of the classification, a feature selection technique using Support Vector Machine-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. Although the algorithm is designed for structural brain image classification, it is readily applicable for functional brain image classification with proper feature images. For simplicity, here we focus on structural brain image classification.



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  • Yong Fan
  • Dinggang Shen
  • Christos Davatzikos



[1] COMPARE: Classification Of Morphological Patterns using Adaptive Regional Elements Yong Fan, Dinggang Shen, Ruben C. Gur, Raquel E. Gur, Christos Davatzikos IEEE Transactions on Medical Imaging, 93-105, Vol. 26, No. 1, 2007

[2] C. Davatzikos, A. Genc, D. Xu, and S. M. Resnick, "Voxel-Based Morphometry Using the RAVENS Maps: Methods and Validation Using Simulated Longitudinal Atrophy," NeuroImage, vol. 14, pp. 1361-1369, 2001.

[3] D. Shen and C. Davatzikos, "HAMMER: Hierarchical attribute matching mechanism for elastic registration," IEEE Transactions on Medical Imaging, vol. 21, pp. 1421-1439, 2002.

[4] P. Golland, W. E. L. Grimson, M. E. Shenton, and R. Kikinis, "Deformation Analysis for Shape Based Classification," presented at the 17th International Conference on Information Processing in Medical Imaging, 2001.

[5] P. Golland, Fischl,B., Spiridon,M., Kanwisher,N., Buckner,R.L., Shenton,M.E., Kikinis,R., Dale,A., and Grimson,W.E.L, "Discriminative Analysis for Image-based Studies," presented at Fifth International Conference on Medical Image Computing and Computer Assisted Intervention, Tokyo, Japan, 2002.

[6] T. Fawcett, "ROC graphs: notes and practical considerations for data mining researchers," HP Laboratories Palo Alto HPL-2003-4,01-07-2003 2003.