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.
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/data ). As such, the investigators within PPMI contributed to the design and implementation of PPMI and/or provided data but did not participate in the analysis or writing of this report. PPMI investigators include (complete listing at PPMI site).
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
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
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
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
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)
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)
The Parkinson Progression Markers Initiative: PPMI. Imaging Technical Operations Manual, 2 edn. (June 2010)
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
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
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)
McGee, S.: Simplifying likelihood ratios. Journal of General Internal Medicine 17(8), 646–649 (2002)
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
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)
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
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)
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)
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
Vapnik, V.N.: Estimation of Dependences Based on Empirical Data. Springer, New York (1982)
Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J., Illán, I.A., Puntonet, C.G. (2013). Texture Features Based Detection of Parkinson’s Disease on DaTSCAN Images. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-38622-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38621-3
Online ISBN: 978-3-642-38622-0
eBook Packages: Computer ScienceComputer Science (R0)