Abstract
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be directly compared since they use different databases and validation protocols. In this paper, we present an extensive study of image descriptors for the diagnosis of Alzheimer Disease (AD) and introduce a new one, named Residual Center of Mass (RCM). The RCM descriptor explores image moments and other techniques to enhance brain regions and select discriminative features for the diagnosis of AD. For validation, a Support Vector Machine (SVM) is trained with the selected features to classify images from normal subjects and patients with AD. We show that RCM with SVM achieves the best accuracies on a considerable number of exams by 10-fold cross-validation — 95.1% on 507 FDG-PET scans and 90.3% on 1374 MRI scans.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alzheimer’s Association. (2017). Alzheimer’s disease and dementia. http://www.alz.org/. [Online; accessed 20 Dec 2017].
Ambastha, A.K. (2015). Neuroanatomical characterisation of Alzheimer’s disease using deep learning. National University of Singapore.
Association, A.E.R., Association, A.P., on Measurement in Education, N.C., on Standards for Educational, J.C., (US), P.T. (1999). Standards for educational and psychological testing. American Educational Research Association.
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Carmichael, O.T., Aizenstein, H.A., Davis, S.W., Becker, J.T., Thompson, P.M., Meltzer, C.C., Liu, Y. (2005). Atlas-based hippocampus segmentation in Alzheimer’s disease and mild cognitive impairment. NeuroImage, 27(4), 979–990.
Casanova, R., Whitlow, C.T., Wagner, B., Williamson, J., Shumaker, S.A., Maldjian, J.A., Espeland, M.A. (2011). High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization. Frontiers in Neuroinformatics, 5, 22.
Chaumette, F. (2004). Image moments: a general and useful set of features for visual servoing. IEEE Transactions on Robotics, 20(4), 713–723.
Chen, Y.W., & Lin, C.J. (2006). Combining SVMs with various feature selection strategies. In Feature extraction (pp. 315–324). Springer.
Chincarini, A., Bosco, P., Calvini, P., Gemme, G., Esposito, M., Olivieri, C., Rei, L., Squarcia, S., Rodriguez, G., Bellotti, R., et al. (2011). Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. NeuroImage, 58(2), 469–480.
Costafreda, S.G., Chu, C., Ashburner, J., Fu, C.H. (2009). Prognostic and diagnostic potential of the structural neuroanatomy of depression. PloS one, 4(7), e6353.
Costafreda, S.G., Fu, C.H., Picchioni, M., Toulopoulou, T., McDonald, C., Kravariti, E., Walshe, M., Prata, D., Murray, R.M., McGuire, P.K. (2011). Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry, 11(1), 1.
Eickhoff, S.B., Stephan, K.E., Mohlberg, H., Grefkes, C., Fink, G.R., Amunts, K., Zilles, K. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage, 25(4), 1325–1335.
Elssied, N.O.F., Ibrahim, O., Osman, A.H. (2014). A novel feature selection based on one-way ANOVA f-test for e-mail spam classification. Research Journal of Applied Sciences Engineering and Technology, 7(3), 625–638.
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L. (2011). Brain development cooperative group, others: unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313–327.
French, A., Macedo, M., Poulsen, J., Waterson, T., Yu, A. (2017). Multivariate analysis of variance (MANOVA). http://userwww.sfsu.edu/efc/classes/biol710/manova/MANOVAnewest.pdf. [Online; accessed 20 Dec 2017].
Garali, I., Adel, M., Bourennane, S., Guedj, E. (2016). Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer’s disease. Biomedical Signal Processing and Control, 27, 15–23.
Golugula, A., Lee, G., Madabhushi, A. (2011). Evaluating feature selection strategies for high dimensional, small sample size datasets. In 2011 Annual International conference of the IEEE engineering in medicine and biology society (pp. 949–952). IEEE.
Grünauer, A., & Vincze, M. (2015). Using dimension reduction to improve the classification of high-dimensional data. arXiv:1505.06907.
Gupta, A., Ayhan, M., Maida, A. (2013). Natural image bases to represent neuroimaging data. In ICML (Vol. 3, pp. 987–994).
Halldestam, M. (2016). ANOVA-the effect of outliers.
Hanley, J.A., & McNeil, B.J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.
Heijmans, H.J., & Roerdink, J. (1998). Mathematical morphology and its applications to image and signal processing (Vol. 12). Springer Science & Business Media.
Illán, I., Górriz, J., Ramírez, J., Salas-Gonzalez, D., López, M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.G., ADNI, et al. (2011). 18 F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences, 181(4), 903–916.
Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., L Whitwell, J., Ward, C., et al. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.
Jenkinson, M., Pechaud, M., Smith, S. (2005). BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh annual meeting of the organization for human brain mapping (Vol. 17, p. 167).
Khedher, L., Ramírez, J., Górriz, J.M., Brahim, A., Segovia, F., ADNI, et al. (2015). Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing, 151, 139–150.
Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., et al. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786–802.
Klöppel, S., Stonnington, C.M., Barnes, J., Chen, F., Chu, C., Good, C.D., Mader, I., Mitchell, L.A., Patel, A.C., Roberts, C.C., et al. (2008). Accuracy of dementia diagnosis - a direct comparison between radiologists and a computerized method. Brain: A Journal of Neurology, 131(11), 2969–2974.
Kramer, O. (2016). Scikit-learn. In Machine learning for evolution strategies (pp. 45–53). Springer.
Landini, L., Positano, V., Santarelli, M. (2005). Advanced image processing in magnetic resonance imaging. CRC Press.
Landis, J.R., & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 159–174.
Liu, M., Zhang, D., Shen, D., ADNI, et al. (2014). Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosis. Neuroinformatics, 12(3), 381–394.
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J., et al. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Transactions on Biomedical Engineering, 62(4), 1132–1140.
Payan, A., & Montana, G. (2015). Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv:1502.02506.
Rao, A., Lee, Y., Gass, A., Monsch, A. (2011). Classification of Alzheimer’s disease from structural MRI using sparse logistic regression with optional spatial regularization. In 2011 Annual International conference of the IEEE engineering in medicine and biology society, EMBC (pp. 4499–4502). IEEE.
Russ, J.C. (2016). The image processing handbook. CRC Press.
Segovia, F., Górriz, J., Ramírez, J., Salas-Gonzalez, D., Álvarez, I., López, M., Chaves, R., ADNI, et al. (2012). A comparative study of feature extraction methods for the diagnosis of Alzheimer’s disease using the ADNI database. Neurocomputing, 75(1), 64–71.
Segovia, F., Ramírez, J., Górriz, J.M., Chaves, R., Salas-Gonzalez, D., López, M., Álvarez, I., Padilla, P., Puntonet, C.G. (2010). Partial least squares for feature extraction of SPECT images. In International Conference on hybrid artificial intelligence systems (pp. 476–483). Springer.
Sensi, F., Rei, L., Gemme, G., Bosco, P., Chincarini, A. (2014). Global disease index, a novel tool for MTL atrophy assessment. In MICCAI workshop challenge on computer-aided diagnosis of dementia based on structural MRI data (pp. 92–100).
Somasundaram, K., & Genish, T. (2014). The extraction of hippocampus from MRI of human brain using morphological and image binarization techniques. In 2014 International Conference on electronics and communication systems (ICECS) (pp. 1–5). IEEE.
Walter, B., Blecker, C., Kirsch, P., Sammer, G., Schienle, A., Stark, R., Vaitl, D. (2003). MARINA: an easy to use tool for the creation of MAsks for Region of INterest analyses. In 9th International conference on functional mapping of the human brain (Vol. 19).
Wenlu, Y., Fangyu, H., Xinyun, C., Xudong, H. (2011). ICA-based automatic classification of PET images from ADNI database. In International Conference on neural information processing (pp. 265–272). Springer.
World Health Organization. (2017). Dementia fact sheet. http://www.who.int/mediacentre/factsheets/fs362/en/. [Online; accessed 20 Dec 2017].
Yang, W., Lui, R.L., Gao, J.H., Chan, T.F., Yau, S.T., Sperling, R.A., Huang, X. (2011). Independent component analysis-based classification of Alzheimer’s disease MRI data. Journal of Alzheimer’s Disease, 24(4), 775–783.
Acknowledgements
We thank Instituto de Pesquisas Eldorado, FAPESP (grant number 14/12236-1) and CNPq (grant number 302970/2014-2) for financial support. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Author information
Authors and Affiliations
Consortia
Corresponding author
Additional information
Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Rights and permissions
About this article
Cite this article
Yamashita, A.Y., Falcão, A.X., Leite, N.J. et al. The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease. Neuroinform 17, 307–321 (2019). https://doi.org/10.1007/s12021-018-9390-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-018-9390-0