Abstract
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the selection of those voxels which present a greater difference between normals and Alzheimer’s type dementia patients. The mean value of the intensities of the selected voxels are used as features for different classifiers. The proposed methodology reaches an accuracy of 89% in the classification task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holman, B.L., Johnson, K.A., Gerada, B., Carvalho, P.A., Satlin, A.: The Scintigraphic Appearance of Alzheimer’s Disease: A Prospective Study Using Technetium-99m-HMPAO SPECT. Journal of Nuclear Medicine 33(2), 181–185 (1992)
Jagust, W., Thisted, R., Devous, M.D., Heertum, R.V., Mayberg, H., Jobst, K., Smith, A.D., Borys, N.: Spect Perfusion Imaging in the Diagnosis of Alzheimer’s Disease: A Clinical-pathologic Study. Neurology 56, 950–956 (2001)
McNeill, R., Sare, G.M., Manoharan, M., Testa, H.J., Mann, D.M.A., Neary, D., Snowden, J.S., Varma, A.R.: Accuracy of Single-photon Emission Computed Tomography in Differentiating Frontotemporal Dementia from Alzheimer’s Disease. J. Neurol. Neurosurg. Psychiatry 78(4), 350–355 (2007)
Ramírez, J., Górriz, J.M., Romero, A., Lassl, A., Salas-Gonzalez, D., López, M., Gómez-Río, M., Rodríguez, A.: Computer Aided Diagnosis of Alzheimer Type Dementia Combining Support Vector Machines and Discriminant Set of Features. Information Sciences (accepted) (2008)
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)
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)
Lassl, A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., Puntonet, C.G., Lang, E.W.: Clustering Approach for the Classification of Spect Images. In: Medical Imaging Conference, Dresden. IEEE, Los Alamitos (2008)
Krzanowski, W.J. (ed.): Principles of Multivariate Analysis: A User’s Perspective. Oxford University Press, Inc., New York (1988)
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)
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)
Ashburner, J., Friston, K.J.: Nonlinear Spatial Normalization Using Basis Functions. Human Brain Mapping 7(4), 254–266 (1999)
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.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 623–630. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salas-Gonzalez, D. et al. (2009). Selecting Regions of Interest for the Diagnosis of Alzheimer Using Brain SPECT Images. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-01513-7_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
eBook Packages: Computer ScienceComputer Science (R0)