Advertisement

The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease

  • Alexandre Yukio Yamashita
  • Alexandre Xavier Falcão
  • Neucimar Jerônimo Leite
  • Alzheimer’s Disease Neuroimaging Initiative
Original Article

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.

Keywords

Diagnosis Neuroimaging Alzheimer disease Machine learning Support vector machine Image analysis Biomarker 

Notes

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.

References

  1. Alzheimer’s Association. (2017). Alzheimer’s disease and dementia. http://www.alz.org/. [Online; accessed 20 Dec 2017].
  2. Ambastha, A.K. (2015). Neuroanatomical characterisation of Alzheimer’s disease using deep learning. National University of Singapore.Google Scholar
  3. 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.Google Scholar
  4. 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.CrossRefGoogle Scholar
  5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. Chaumette, F. (2004). Image moments: a general and useful set of features for visual servoing. IEEE Transactions on Robotics, 20(4), 713–723.CrossRefGoogle Scholar
  9. Chen, Y.W., & Lin, C.J. (2006). Combining SVMs with various feature selection strategies. In Feature extraction (pp. 315–324). Springer.Google Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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].
  17. 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.CrossRefGoogle Scholar
  18. 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.Google Scholar
  19. Grünauer, A., & Vincze, M. (2015). Using dimension reduction to improve the classification of high-dimensional data. arXiv:1505.06907.
  20. Gupta, A., Ayhan, M., Maida, A. (2013). Natural image bases to represent neuroimaging data. In ICML (Vol. 3, pp. 987–994).Google Scholar
  21. Halldestam, M. (2016). ANOVA-the effect of outliers.Google Scholar
  22. 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.CrossRefGoogle Scholar
  23. Heijmans, H.J., & Roerdink, J. (1998). Mathematical morphology and its applications to image and signal processing (Vol. 12). Springer Science & Business Media.Google Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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).Google Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. Kramer, O. (2016). Scikit-learn. In Machine learning for evolution strategies (pp. 45–53). Springer.Google Scholar
  31. Landini, L., Positano, V., Santarelli, M. (2005). Advanced image processing in magnetic resonance imaging. CRC Press.Google Scholar
  32. Landis, J.R., & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 159–174.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. Payan, A., & Montana, G. (2015). Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv:1502.02506.
  36. 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.Google Scholar
  37. Russ, J.C. (2016). The image processing handbook. CRC Press.Google Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.Google Scholar
  40. 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).Google Scholar
  41. 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.Google Scholar
  42. 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).Google Scholar
  43. 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.Google Scholar
  44. World Health Organization. (2017). Dementia fact sheet. http://www.who.int/mediacentre/factsheets/fs362/en/. [Online; accessed 20 Dec 2017].
  45. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Software BInstituto de Pesquisas EldoradoCampinasBrazil
  2. 2.Institute of ComputingUniversity of CampinasCampinasBrazil

Personalised recommendations