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2017 | OriginalPaper | Chapter

Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow

Authors : Fermín Segovia, Marcelo García-Pérez, Juan Manuel Górriz, Javier Ramírez, Francisco Jesús Martínez-Murcia

Published in: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Publisher: Springer International Publishing

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Abstract

Neuroimaging data provides a valuable tool to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). During last years many research efforts have focused on the development of computer systems that automatically analyze neuroimaging data and allow improving the diagnosis of those diseases. This field has benefited from modern machine learning techniques, which provide a higher generalization ability, however the high dimensionality of the data is still a challenge and there is room for improvement. In this work we demonstrate a computer system based on Principal Component Analysis and TensorFlow, the machine learning library recently released by Google. The proposed system is able to successfully separate AD or PD patients from healthy subjects, as well as distinguishing between PD and other parkinsonian syndromes. The obtained results suggest that TensorFlow is a suitable environment to classify neuroimaging data and can help to improve the diagnosis of AD and Parkinsonism.

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Literature
1.
2.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://​tensorflow.​org/​, software available from tensorow.org
3.
Bach, J., Ziegler, U., Deuschl, G., Dodel, R., Doblhammer-Reiter, G.: Projected numbers of people with movement disorders in the years 2030 and 2050. Mov. Disord. 26(12), 2286–2290 (2011)CrossRef
4.
Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. J. Alzheimer’s Assoc. 3(3), 186–191 (2007)CrossRef
5.
Duin, R.: Classifiers in almost empty spaces. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 1–7 (2000)
6.
Foster, N.L., Heidebrink, J.L., Clark, C.M., Jagust, W.J., Arnold, S.E., Barbas, N.R., DeCarli, C.S., Turner, R.S., Koeppe, R.A., Higdon, R., Minoshima, S.: FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain 130(10), 2616–2635 (2007)CrossRef
7.
Fougère, C.I., Pöpperl, G., Levin, J., Wängler, B., Böning, G., Uebleis, C., Cumming, P., Bartenstein, P., Bötzel, K., Tatsch, K.: The value of the dopamine D2/3 receptor ligand 18F-Desmethoxyfallypride for the differentiation of idiopathic and nonidiopathic parkinsonian syndromes. J. Nucl. Med. 51(4), 581–587 (2010)CrossRef
8.
Friston, K., Büchel, C.: Functional connectivity: eigenimages and multivariate analyses. In: Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W. (eds.) Statistical Parametric Mapping, Chap. 37, pp. 492–507. Academic Press, London (2007)CrossRef
9.
Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images, 1st edn. Academic Press, Amsterdam, Boston (2006)
10.
Gilman, S., Wenning, G.K., Low, P.A., Brooks, D.J., Mathias, C.J., Trojanowski, J.Q., Wood, N.W., Colosimo, C., Dürr, A., Fowler, C.J., Kaufmann, H., Klockgether, T., Lees, A., Poewe, W., Quinn, N., Revesz, T., Robertson, D., Sandroni, P., Seppi, K., Vidailhet, M.: Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(9), 670–676 (2008)CrossRef
11.
Hughes, A.J., Daniel, S.E., Ben-Shlomo, Y., Lees, A.J.: The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 125(4), 861–870 (2002)CrossRef
12.
Illán, I.A., Górriz, J.M., Ramírez, J., Segovia, F., Jiménez-Hoyuela, J.M., Lozano, S.J.O.: Automatic assistance to parkinson’s disease diagnosis in DaTSCAN SPECT imaging. Med. Phys. 39(10), 5971–5980 (2012)CrossRef
13.
Koch, W., Radau, P.E., Hamann, C., Tatsch, K.: Clinical testing of an optimized software solution for an automated, observer-independent evaluation of dopamine transporter SPECT studies. J. Nucl. Med. 46(7), 1109–1118 (2005)
14.
Litvan, I., Agid, Y., Calne, D., Campbell, G., Dubois, B., Duvoisin, R.C., Goetz, C.G., Golbe, L.I., Grafman, J., Growdon, J.H., Hallett, M., Jankovic, J., Quinn, N.P., Tolosa, E., Zee, D.S.: Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology 47(1), 1–9 (1996)CrossRef
15.
Lopez, M., Ramirez, J., Gorriz, J., Salas-Gonzalez, D., Alvarez, I., Segovia, F., Puntonet, C.G.: Automatic tool for Alzheimer’s disease diagnosis using PCA and bayesian classification rules. Electron. Lett. 45(8), 389–391 (2009)CrossRef
16.
Murray, D.G., McSherry, F., Isaacs, R., Isard, M., Barham, P., Abadi, M.: Naiad: a timely dataflow system. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP 2013, pp. 439–455. ACM, New York (2013)
17.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
18.
Saxena, P., Pavel, D.G., Quintana, J.C., Horwitz, B.: An automatic threshold-based scaling method for enhancing the usefulness of Tc-HMPAO SPECT in the diagnosis of Alzheimer’s disease. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 623–630. Springer, Heidelberg (1998). doi:10.​1007/​BFb0056248CrossRef
19.
Segovia, F., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., Álvarez, I., López, M., Chaves, R.: A comparative study of feature extraction methods for the diagnosis of Alzheimer’s disease using the ADNI database. Neurocomputing 75(1), 64–71 (2012)CrossRef
20.
Segovia, F., Bastin, C., Salmon, E., Górriz, J.M., Ramírez, J., Phillips, C.: Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer’s disease. PLoS ONE 9(2), e88687 (2014)CrossRef
21.
Towey, D.J., Bain, P.G., Nijran, K.S.: Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nucl. Med. Commun. 32(8), 699–707 (2011)CrossRef
22.
Trambaiolli, L.R., Lorena, A.C., Fraga, F.J., Kanda, P.A.M., Anghinah, R., Nitrini, R.: Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42(3), 160–165 (2011)CrossRef
Metadata
Title
Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow
Authors
Fermín Segovia
Marcelo García-Pérez
Juan Manuel Górriz
Javier Ramírez
Francisco Jesús Martínez-Murcia
Copyright Year
2017
Book
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Print ISBN: 978-3-319-47363-5

Electronic ISBN: 978-3-319-47364-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47364-2_5

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