Automated Diagnosis of Parkinsonian Syndromes by Deep Sparse Filtering-Based Features

  • Andrés OrtizEmail author
  • Francisco J. Martínez-Murcia
  • María J. García-Tarifa
  • Francisco Lozano
  • Juan M. Górriz
  • Javier Ramírez
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 60)


Parkinsonian Syndrome (PS) or Parkinsonism is the second most common neurodegenerative disorder in the elderly. Currently there is no cure for PS, and it has important socio-economic implications due to the fact that PS progressively disables people in their ordinary daily tasks. However, precise and early diagnosis can definitely help to start the treatment in the early stages of the disease, improving the patient’s quality of life. The study of neurodegenerative diseases has been usually addressed by visual inspection and semi-quantitative analysis of medical imaging, which results in subjective outcomes. However, recent advances in statistical signal processing and machine learning techniques provide a new way to explore medical images yielding to an objective analysis, dealing with the Computer Aided Diagnosis (CAD) paradigm. In this work, we propose a method that selects the most discriminative regions on 123I-FP-CIT SPECT (DaTSCAN) images and learns features using deep-learning techniques. The proposed system has been tested using images from the Parkinson Progression Markers Initiative (PPMI), obtaining accuracy values up to 95 %, showing its robustness for PS pattern detection and outperforming the baseline Voxels-as-Features (VAF) approach, used as an approximation of the visual analysis.


Independent Component Analysis Multiple System Atrophy Essential Tremor Independent Component Analysis Deep Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partly supported by the MINECO under the TEC2015-64718-R and PSI2015-65848-R projects and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the P11-TIC-7103 Excellence Project. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genetech, GlaxoSmithKline, Eli Lilly and Co., Lundbeck, Merck, MSD Meso Scale Discovery, Pfizer, Piramal, Roche, Servier and UCB.


  1. 1.
    Abdi, H., Williams, L.J.: Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 433–459 (2010)Google Scholar
  2. 2.
    Álvarez, I., Górriz, J.M., Ramírez, J., Salas-González, D., López, M., Segovia, F., Padilla, P., García, C.: Projecting independent components of spect images for computer aided diagnosis of Alzheimer’s disease. Pattern Recogn. Lett. 31(11), 1342–1347 (2010)Google Scholar
  3. 3.
    Álvarez, I., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Lopez, M.M., Segovia, F., Chaves, R., Gomez-Rio, M., Garcia-Puntonet, C.: 18f-fdg pet imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 184(4), 903–916 (2011)Google Scholar
  4. 4.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Montréal, Université, Québec, Montréal: Greedy Layer-Wise Training of Deep Networks. MIT Press, In NIPS (2007)Google Scholar
  5. 5.
    de Lau, L.M., Breteler, M.M.: Epidemiology of parkinson’s disease. Lancet Neurol 5(6), 525–535 (2006)Google Scholar
  6. 6.
    Górriz, J.M., Segovia, F., Ramírez, J., Lassl, A., Salas-González, D.: Gmm based spect image classification for the diagnosis of Alzheimer’s disease. Appl. Soft Comput. 11, 2313–2325 (2011)CrossRefGoogle Scholar
  7. 7.
    Graña, M.: Towards relevance dendritic computing. In: Proceedings of the Nature and Biologically Inspired Computing (NaBIC), pp. 588–593 (2011)Google Scholar
  8. 8.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)Google Scholar
  9. 9.
    López, M., Ramírez, J., Górriz, J.M., Álvarez, I., Salas-González, D., Segovia, F., Chaves, R., Padilla, P., Gómez-Río, M.: Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease. Neurocomputing 74(8), 1260–1271 (2011)CrossRefGoogle Scholar
  10. 10.
    Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., Poewe, W., Mollenhauer, B., Sherer, T., Frasier, M., Meunier, C., Rudolph, A., Casaceli, C., Seibyl, J., Mendick, S., Schuff, N., Zhang, Y., Toga, A., Crawford, K., Ansbach, A., de Blasio, P., Piovella, M., Trojanowski, J., Shaw, L., Singleton, A., Hawkins, K., Eberling, J., Russell, D., Leary, L., Factor, S., Sommerfeld, B., Hogarth, P., Pighetti, E., Williams, K., Standaert, D., Guthrie, S., Hauser, R., Delgado, H., Jankovic, J., Hunter, C., Stern, M., Tran, B., Leverenz, J., Baca, M., Frank, S., Thomas, C.A., Richard, I., Deeley, C., Rees, L., Sprenger, F., Lang, E., Shill, H., Obradov, S., Fernandez, H., Winters, A., Berg, D., Gauss, K., Galasko, D., Fontaine, D., Mari, Z., Gerstenhaber, M., Brooks, D., Malloy, S., Barone, P., Longo, K., Comery, T., Ravina, B., Grachev, I., Gallagher, K., Collins, M., Widnell, K.L., Ostrowizki, S., Fontoura, P., La-Roche, F.H., Ho, T., Luthman, J., van der Brug, M., Reith, A.D., Taylor, P.: The Parkinson progression marker initiative (PPMI). Progr. Neurobiol. 95(4), 629–635 (2011)Google Scholar
  11. 11.
    Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J., Illán, I.A., Ortiz, A.: Automatic detection of parkinsonism using significance measures and component analysis in datscan imaging. Neurocomputing 126, 58–70 (2014)Google Scholar
  12. 12.
    Martinez-Murcia, F.J., Górriz, J.M., Ramírez, J., Moreno-Caballero, M., Gómez-Río, M.: Parametrization of textural patterns in 123i-ioflupane imaging for the automatic detection of parkinsonism. Med. Phys. 41(1), 012502 (2014)Google Scholar
  13. 13.
    Ngiam, J., Chen, Z., Bhaskar, S.A., Koh, P.W., Ng, A.Y.: Sparse filtering. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 1125–1133. Curran Associates, Inc. (2011)Google Scholar
  14. 14.
    Ortega Lozano, S.J., Martinez Del Valle Torres, M.D., Ramos Moreno, E., Sanz Viedma, S., Amrani Raissouni, T., Jiménez-Hoyuela, J.M.: Quantitative evaluation of spect with fp-cit. importance of the reference area. Rev. Esp. Med. Nucl. 29(5), 246–250 (2010)Google Scholar
  15. 15.
    Politis, M.: Neuroimaging in parkinson disease: from research setting to clinical practice. Nat. Rev. Neurol. 10(12), 708–722 (2014)Google Scholar
  16. 16.
    Raja, K.B., Raghavendra, R., Krishna Vemuri, V., Busch, C.: Smartphone based visible iris recognition using deep sparse filtering. Pattern Recogn. Lett. 57, 33-42 (2015)Google Scholar
  17. 17.
    Segovia, F., Manuel Górriz, J., Ramírez, J., Chaves, R., Álvarez Illán, I.: Automatic differentiation between controls and parkinson’s disease datscan images using a partial least squares scheme and the fisher discriminant ratio. In: KES, pp. 2241–2250 (2012)Google Scholar
  18. 18.
    The Parkinson Progression Markers Initiative: PPMI. Imaging Technical Operations Manual, 2nd edn (2010)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Vladimir, N.: Vapnik. Wiley-Interscience, Statistical Learning Theory (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrés Ortiz
    • 1
    Email author
  • Francisco J. Martínez-Murcia
    • 2
  • María J. García-Tarifa
    • 1
  • Francisco Lozano
    • 1
  • Juan M. Górriz
    • 2
  • Javier Ramírez
    • 2
  1. 1.Communications Engineering DepartmentUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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