Advertisement

Texture Features Based Detection of Parkinson’s Disease on DaTSCAN Images

  • Francisco Jesús Martínez-Murcia
  • Juan Manuel Górriz
  • Javier Ramírez
  • I. Alvarez Illán
  • C. G. Puntonet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)

Abstract

In this work, a novel approach to Computer Aided Diagnosis (CAD) system for the Parkinson’s Disease (PD) is proposed. This tool is intended for physicians, and is based on fully automated methods that lead to the classification of Ioflupane/FP-CIT-I-123 (DaTSCAN) SPECT images. DaTSCAN images from the Parkinson Progression Markers Initiative (PPMI) are used to have in vivo information of the dopamine transporter density. These images are normalized, reduced (using a mask), and then a GLC matrix is computed over the whole image, extracting several Haralick texture features which will be used as a feature vector in the classification task. Using the leave-one-out cross-validation technique over the whole PPMI database, the system achieves results up to a 95.9% of accuracy, and 97.3% of sensitivity, with positive likelihood ratios over 19, demonstrating our system’s ability on the detection of the Parkinson’s Disease by providing robust and accurate results for clinical practical use, as well as being fast and automatic.

Keywords

Parkinson’s Disease DaTSCAN images Computer Aided Diagnosis Haralick Texture Features Support Vector Machines Supervised Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bhidayasiri, R.: How useful is (123I) beta-CIT SPECT in the diagnosis of parkinson’s disease? Reviews in Neurological Diseases 3(1), 19–22 (2006) PMID: 16596082, http://www.ncbi.nlm.nih.gov/pubmed/16596082 Google Scholar
  2. 2.
    Christine, C.W., Aminoff, M.J.: Clinical differentiation of parkinsonian syndromes: Prognostic and therapeutic relevance. The American Journal of Medicine 117(6), 412–419 (2004), http://www.sciencedirect.com/science/article/pii/S0002934304003626 CrossRefGoogle Scholar
  3. 3.
    Eckert, T., Edwards, C.: The application of network mapping in differential diagnosis of parkinsonian disorders. Clinical Neuroscience Research 6(6), 359–366 (2007), neural Networks in the Imaging of Neuropsychiatric Diseases, http://www.sciencedirect.com/science/article/pii/S1566277207000023 CrossRefGoogle Scholar
  4. 4.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    Illán, I., Górriz, J., Ramírez, J., Segovia, F., Jiménez-Hoyuela, J., Ortega Lozano, S.: Automatic assistance to parkinsons disease diagnosis in datscan spect imaging. Medical Physics 39(10), 5971–5980 (2012)CrossRefGoogle Scholar
  6. 6.
    The Parkinson Progression Markers Initiative: PPMI. Imaging Technical Operations Manual, 2 edn. (June 2010)Google Scholar
  7. 7.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference on AI, pp. 1137–1145 (1995), http://citeseer.ist.psu.edu/kohavi95study.html
  8. 8.
    Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., Poewe, W., Mollenhauer, B., Klinik, P., 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., Brooks, D., 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., 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., Ho, T., Luthman, J., van der Brug, M., Reith, A.D., Taylor, P.: The parkinson progression marker initiative (PPMI). Progress in Neurobiology 95(4), 629–635 (2011), http://www.sciencedirect.com/science/article/pii/S0301008211001651 CrossRefGoogle Scholar
  9. 9.
    Martínez-Murcia, F., Górriz, J., Ramírez, J., Puntonet, C., Salas-González, D.: Computer aided diagnosis tool for Alzheimer’s disease based on Mann-Whitney-Wilcoxon U-test. Expert Systems with Applications 39(10), 9676–9685 (2012)CrossRefGoogle Scholar
  10. 10.
    McGee, S.: Simplifying likelihood ratios. Journal of General Internal Medicine 17(8), 646–649 (2002)CrossRefGoogle Scholar
  11. 11.
    Moghal, S., Rajput, A.H., D’Arcy, C., Rajput, R.: Prevalence of movement disorders in elderly community residents. Neuroepidemiology 13(4), 175–178 (1994) PMID: 8090259, http://www.ncbi.nlm.nih.gov/pubmed/8090259 CrossRefGoogle Scholar
  12. 12.
    Philips, C., Li, D., Raicu, D., Furst, J.: Directional invariance of co-occurrence matrices within the liver. In: International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, pp. 29–34 (2008)Google Scholar
  13. 13.
    Rojas, A., Górriz, J., Ramírez, J., Illán, I., Martínez-Murcia, F., Ortiz, A., Río, M.G., Moreno-Caballero, M.: Application of empirical mode decomposition (emd) on datscan spect images to explore parkinson disease. Expert Systems with Applications 40(7), 2756–2766 (2013), http://www.sciencedirect.com/science/article/pii/S0957417412012274 CrossRefGoogle Scholar
  14. 14.
    Segovia, F., Górriz, J.M., Ramírez, J., Álvarez, I., Jiménez-Hoyuela, J.M., Ortega, S.J.: Improved parkinsonism diagnosis using a partial least squares based approach. Medical Physics 39(7), 4395–4403 (2012)CrossRefGoogle Scholar
  15. 15.
    Stoeckel, J., Ayache, N., Malandain, G., Malick Koulibaly, P., Ebmeier, K.P., Darcourt, J.: Automatic classification of SPECT images of alzheimer’s disease patients and control subjects. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 654–662. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Towey, D.J., Bain, P.G., Nijran, K.S.: Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nuclear Medicine Communications 32(8), 699–707 (2011) PMID: 21659911, http://www.ncbi.nlm.nih.gov/pubmed/21659911 CrossRefGoogle Scholar
  17. 17.
    Vapnik, V.N.: Estimation of Dependences Based on Empirical Data. Springer, New York (1982)zbMATHGoogle Scholar
  18. 18.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francisco Jesús Martínez-Murcia
    • 1
  • Juan Manuel Górriz
    • 1
  • Javier Ramírez
    • 1
  • I. Alvarez Illán
    • 1
  • C. G. Puntonet
    • 2
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversidad of GranadaSpain
  2. 2.Department of Computer Architecture and TechnologyUniversidad de GranadaSpain

Personalised recommendations