Abstract
Pattern classification methods have been widely studied for analysis of brain images to decode the disease states, such as diagnosis of Alzheimer’s disease (AD). Most existing methods aimed to extract discriminative features from neuroimaging data and then build a supervised classifier for classification. However, due to the rich imaging features and small sample size of neuroimaging data, it is still challenging to make use of features to achieve good classification performance. In this paper, we propose a hierarchical ensemble classification algorithm to gradually combine the features and decisions into a unified model for more accurate classification. Specifically, a number of low-level classifiers are first built to transform the rich imaging and correlation-context features of brain image into more compact high-level features with supervised learning. Then, multiple high-level classifiers are generated, with each evaluating the high-level features of different brain regions. Finally, all high-level classifiers are combined to make final decision. Our method is evaluated using MR brain images on 427 subjects (including 198 AD patients and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our method achieves an accuracy of 92.04% and an AUC (area under the ROC curve) of 0.9518 for AD classification, demonstrating very promising classification performance.
This work was partially supported by NIH grants EB006733, EB008374, EB009634 and MH088520, NSFC grants (No. 61005024 and 60875030), and Medical and Engineering Foundation of Shanghai Jiao Tong University (No. YG2010MS74).
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References
Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C.: Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48, 138–149 (2009)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)
Davatzikos, C., Fan, Y., Wu, X., Shen, D., Resnick, S.M.: Detection of Prodromal Alzheimer’s Disease via Pattern Classification of MRI. Neurobiol Aging (2006) (epub.)
Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehericy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766–781 (2011)
Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements. IEEE Trans. Med. Imaging 26, 93–105 (2007)
Ishii, K., Kawachi, T., Sasaki, H., Kono, A.K., Fukuda, T., Kojima, Y., Mori, E.: Voxel-based morphometric comparison between early- and late-onset mild Alzheimer’s disease and assessment of diagnostic performance of z score images. American Journal of Neuroradiology 26, 333–340 (2005)
Davatzikos, C., Resnick, S.M., Wu, X., Parmpi, P., Clark, C.M.: Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41, 1220–1227 (2008)
Vemuri, P., Gunter, J.L., Senjem, M.L., Whitwell, J.L., Kantarci, K., Knopman, D.S., Boeve, B.F., Petersen, R.C., Jack Jr., C.R.: Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies. Neuroimage 39, 1186–1197 (2008)
Zhou, L., Wang, Y., Li, Y., Yap, P.T., Shen, D.: Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures. Plos One 6, e21935 (2011)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87–97 (1998)
Shen, D., Davatzikos, C.: Very high resolution morphometry using mass-preserving deformations and HAMMER elastic registration. Neuroimage 18, 28–41 (2003)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Information Fusion 6, 5–20 (2005)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6, 63–81 (2005)
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Liu, M., Zhang, D., Yap, PT., Shen, D. (2012). Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer’s Disease. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_4
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DOI: https://doi.org/10.1007/978-3-642-35428-1_4
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