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.
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Giuliania, N.R., Calhoun, V.D., Pearlson, G.D., Francisd, A., Buchanan, R.W.: Voxel-based morphometry versus region of interest: a comparisonof two methods for analyzing gray matter differences in schizophrenia. Schizophrenia Research 74, 135–147 (2005)
Thompson, P.M., MacDonald, D., Mega, M.S., Holmes, C.J., Evans, A., Toga, A.W.: Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical. Journal of Computer Assisted Tomography 21, 567–581 (1997)
Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. NeuroImage 11, 805–821 (2000)
Chung, M.K., Worsley, K.J., Paus, T., Cherif, C., Collins, D.L., Giedd, J.N., Rapoport, J.L., Evanst, A.C.: A unified statistical approach to deformation-based morphometry. NeuroImage 14, 595–606 (2001)
Davatzikos, C., Genc, A., Xu, D., Resnick, S.M.: Voxel-based morphometry using the ravens maps: Methods and validation using simulated longitudinal atrophy. NeuroImage 14, 1361–1369 (2001)
Miller, M., Banerjee, A., Christensen, G., Joshi, S., Khaneja, N., Grenander, U., Matejic, L.: Statistical methods in computational anatomy. Statistical Methods in Medical Research 6, 267–299 (1997)
Golland, P., Grimson, W.E.L., Shenton, M.E., Kikinis, R.: Deformation analysis for shape based classification. In: IPMI, pp. 517–530 (2001)
Gerig, G., Styner, M., Lieberman, J.: Shape versus Size: Improved understanding of the morphology of brain structures. In: MICCAI, pp. 24–32 (2001)
Yushkevich, P.A., Joshi, S., Pizer, S.M., Csernansky, J.G., Wang, L.E.: Feature selection for shape-based classification of biological objects. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 114–125. Springer, Heidelberg (2003)
Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M.E., Carter, C.S., Stenger, V.A., Davis, S., Aizenstein, H.J., Becker, J.T., Lopez, O.L., Meltzer, C.C.: Discriminative MR image feature analysis for automatic schizophrenia and alzheimer’s disease classification. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 393–401. Springer, Heidelberg (2004)
Lao, Z., Shen, D., Xue, Z., Karacali, B., Resnick, M.S., Davatzikos, C.: Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 21, 46–57 (2004)
Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18, 737–752 (1999)
Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21, 1421–1439 (2002)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
McGraw, K.O., Wong, S.P.: Forming inferences about some intraclass correlation coefficients. Psychological Methods 1, 30–46 (1996)
Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 583–589 (1991)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Rakotomamonjy, A.: Variable selection using SVM-based criteria. Journal of Machine Learning Research 3, 357–1370 (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)
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Fan, Y., Shen, D., Davatzikos, C. (2005). Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_1
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DOI: https://doi.org/10.1007/11566465_1
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