Abstract
An on-going study in Hospital de Santiago Apostol collects anatomical T1-weighted MRI volumes and Diffusion Weighted Imaging (DWI) data of control and Alzheimer’s Disease patients. The aim of this paper is to obtain discriminant features from scalar measures of DWI data, the Fractional Anisotropy (FA) and Mean Diffusivity (MD) volumes, and to train and test classifiers able to discriminate AD patients from controls on the basis of features selected from the FA or MD volumes. In this study, separate classifiers were trained and tested on FA and MD data. Feature selection is done according to the Pearson’s correlation between voxel values across subjects and the control variable giving the subject class (1 for AD patients, 0 for controls). Some of the tested classifiers reach very high accuracy with this simple feature selection process. Those results point to the validity of DWI data as a image-marker for AD.
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
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophysical Journal 66(1), 259–267 (1994); PMID: 8130344 PMCID: 1275686
Behrens, T.E.J., Woolrich, M.W., Jenkinson, M., Johansen-Berg, H., Nunes, R.G., Clare, S., Matthews, P.M., Brady, J.M., Smith, S.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine 50(5), 1077–1088 (2003)
Douaud, G., Jbabdi, S., Behrens, T.E.J., Menke, R.A., Gass, A., Monsch, A.U., Rao, A., Whitcher, B., Kindlmann, G., Matthews, P.M., Smith, S.: DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild alzheimer’s disease. NeuroImage (in Press) Uncorrected Proof
Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging 26(1), 93–105 (2007); PMID: 17243588
García-Sebastián, M., Savio, A., Graña, M., Villanúa, J.: On the use of morphometry based features for alzheimer’s disease detection on MRI. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 957–964. Springer, Heidelberg (2009)
Parente, D.B., Gasparetto, E.L., da Cruz, L.C.H., Domingues, R.C., Baptista, A.C., Carvalho, A.C.P., Domingues, R.C.: Potential role of diffusion tensor MRI in the differential diagnosis of mild cognitive impairment and alzheimer’s disease. Am. J. Roentgenol. 190(5), 1369–1374 (2008)
Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G.: Diffusion tensor MR imaging of the human brain. Radiology 201(3), 637–648 (1996)
Serra, L., Cercignani, M., Lenzi, D., Perri, R., Fadda, L., Caltagirone, C., Macaluso, E., Bozzali, M.: Grey and white matter changes at different stages of alzheimer’s disease. Journal of Alzheimer’s Disease: JAD 19(1), 147–159 (2010); PMID: 20061634
Stahl, R., Dietrich, O., Teipel, S.J., Hampel, H., Reiser, M.F., Schoenberg, S.O.: White matter damage in alzheimer disease and mild cognitive impairment: Assessment with Diffusion-Tensor MR imaging and parallel imaging techniques1. Radiology 243(2), 483–492 (2007)
Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1(3), 211–244 (2001)
Tipping, M.E., Faul, A., Thomson Avenue, J.J., Thomson Avenue, J.J.: Fast marginal likelihood maximisation for sparse bayesian models. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, pp. 3–6 (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Termenon, M., Besga, A., Echeveste, J., Gonzalez-Pinto, A., Graña, M. (2011). Alzheimer Disease Classification on Diffusion Weighted Imaging Features. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)