Abstract
Finding sensitive and appropriate technologies for non-invasive observation and early detection of the Alzheimer’s Type Dementia (ATD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis, rating them by visual evaluations. In this work, we present a computer aided diagnosis method in which a selection of relevant features was extracted from each patient image by means of Independent Component Analysis (ICA). An average image was computed within the normal or Alzheimer’s disease brain image class, to be later used to extract a set of independent sources that best symbolized each class characteristics. Each brain image was projected onto the space spanned by this independent sources basis, and the extracted information was used to train a SVM classifier which could classify new subjects in a unsupervised manner.
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References
Ishii, K., Kono, A.K., Sasaki, H., Miyamoto, N., Fukuda, T., Sakamoto, S., Mori, E.: Fully Automatic Diagnostic System for Early- and Late-onset Mild Alzheimer’s Disease Using FDG PET and 3D-SSP. European Journal of Nuclear Medicine and Molecular Imaging 33(5), 575–583 (2006)
Fung, G., Stoeckel, J.: SVM Feature Selection for Classification of SPECT Images of Alzheimer’s Disease Using Spatial Information. Knowledge and Information Systems 11(2), 243–258 (2007)
Duin, R.P.W.: Classifiers in Almost Empty Spaces. In: Proceedings 15th International Conference on Pattern Recognition, vol. 2, pp. 1–7. IEEE, Los Alamitos (2000)
Nobili, F., Salmaso, D., Morbelli, S., Girtler, N., Piccardo, A., Brugnolo, A., Dessi, B., Larsson, S.A., Rodriguez, G., Pagani, M.: Principal Component Analysis of fdg pet in Amnestic MCI. Eur. J. Nucl. Med. Mol. Imaging 35(12), 2191–2202 (2008)
Bartlett, M., Movellan, J., Sejnowski, T.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)
Theis, F.J., Gruber, P., Keck, I.R., Lang, E.W.: Functional MRI analysis by a novel spatiotemporal ICA algorithm. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 677–682. Springer, Heidelberg (2005)
Fink, F., Worle, K., Gruber, P., Tome, A.M., Gorriz, J.M., Puntonet, C.G., Lang, E.W.: Ica Analysis of Retina Images for Glaucoma Classification. In: 30th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 4664–4667. IEEE, Los Alamitos (2008)
Ramírez, J., Górriz, J.M., Gómez-Río, M., Romero, A., Chaves, R., Lassl, A., Rodríguez, A., Puntonet, C.G., Theis, F., Lang, E.: Effective emission tomography image reconstruction algorithms for SPECT data. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 741–748. Springer, Heidelberg (2008)
Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1998)
Ramírez, J., Yélamos, P., Górriz, J.M., Segura, J.C.: SVM-based Speech Endpoint Detection Using Contextual Speech Features. Electronics Letters 42(7), 877–879 (2006)
Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-based Relevance Feedback in Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2006)
Górriz, J.M., Ramírez, J., Lassl, A., Salas-Gonzalez, D., Lang, E.W., Puntonet, C.G., Álvarez, I., López, M., Gómez-Río, M.: Automatic Computer Aided Diagnosis Tool Using Component-based SVM. In: Medical Imaging Conference, Dresden. IEEE, Los Alamitos (2008)
Comon, P.: Independent Component Analysis, a new concept? Signal Process. 36(3), 287–314 (1994)
Bingham, E.: Advances in Independent Component Analysis with Applications to Data Mining. PhD thesis, Helsinki University of Technology (2003)
Oja, E.: A Fast Fixed-point Algorithm for Independent Component Analysis. Neural Computation 9, 1483–1492 (1997)
Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W. (eds.): Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, London (2007)
Salas-Gonzalez, D., Górriz, J.M., Ramírez, J., Lassl, A., Puntonet, C.G.: Improved Gauss-newton Optimization Methods in Affine Registration of Spect Brain Images. IET Electronics Letters 44(22), 1291–1292 (2008)
Stoeckel, J., Malandain, G., Migneco, O., Koulibaly, P.M., Robert, P., Ayache, N., Darcourt, J.: Classification of SPECT Images of Normal Subjects Versus Images of Alzheimer’s Disease Patients. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 666–674. Springer, Heidelberg (2001)
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Álvarez, I. et al. (2009). Independent Component Analysis of SPECT Images to Assist the Alzheimer’s Disease Diagnosis. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_43
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DOI: https://doi.org/10.1007/978-3-642-01216-7_43
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