Abstract
Alzheimer’s Disease (AD) is nowadays the most common type of dementia, with more than 35.6 million people affected, and 7.7 million new cases every year. Magnetic Resonance Imaging (MRI) is a fairly widespread tool used in clinical practice, and has repeatedly proven its utility in the diagnosis of AD. Therefore a number of automatic methods have been developed for the processing of MR images. In this work, a new algorithm that projects the three-dimensional image onto two-dimensional maps using Local Binary Patterns (LBP) is presented. The algorithm yields visually-assessable maps that contain the textural information and achieves up to a 90.5% accuracy in a differential diagnosis task (AD vs controls), which proves that the textural information retrieved by our methodology is significantly linked to the disease.
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.
Similar content being viewed by others
References
Schroeter, M.L., Stein, T., Maslowski, N., Neumann, J.: Neural correlates of alzheimers disease and mild cognitive impairment: A systematic and quantitative meta-analysis involving 1351 patients. NeuroImage 47(4), 1196–1206 (2009)
Ayache, N.: Analyzing 3D Images of the Brain. NeuroImage 4(3), S34–S35 (1996)
Shiino, A., Watanabe, T., Maeda, K., Kotani, E., Akiguchi, I., Matsuda, M.: Four subgroups of Alzheimer’s disease based on patterns of atrophy using VBM and a unique pattern for early onset disease. NeuroImage 33(1), 17–26 (2006)
Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al.: Reliability of mri-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1), 180–194 (2006)
Kovalev, V.A., Kruggel, F., Gertz, H.J., von Cramon, D.Y.: Three-dimensional texture analysis of mri brain datasets. IEEE Transactions on Medical Imaging 20(5), 424–433 (2001)
Fan, Y., Rao, H., Hurt, H., Giannetta, J., Korczykowski, M., Shera, D., Avants, B.B., Gee, J.C., Wang, J., Shen, D.: Multivariate examination of brain abnormality using both structural and functional MRI. NeuroImage 36(4), 1189–1199 (2007)
Ortiz, A., Górriz, J.M., Ramírez, J., Martínez-Murcia, F.: Lvq-SVM based CAD tool applied to structural MRI for the diagnosis of the alzheimer’s disease. Pattern Recognition Letters 34(14), 1725–1733 (2013)
Yoon, U., Lee, J.M., Im, K., Shin, Y.W., Cho, B.H., Kim, I.Y., Kwon, J.S., Kim, S.I.: Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. NeuroImage 34(4), 1405–1415 (2007)
Unay, D., Ekin, A., Cetin, M., Jasinschi, R., Ercil, A.: Robustness of local binary patterns in brain mr image analysis. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (August 2007)
Martinez-Murcia, F., Górriz, J., Ramírez, J., Moreno-Caballero, M., Gómez-Río, M., Initiative, P.P.M.: Parametrization of textural patterns in 123i-ioflupane imaging for the automatic detection of parkinsonism. Medical Physics 41(1), 012502 (2014)
Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J., Alvarez Illán, I., Salas-González, D., Segovia, F.: Alzheimer’s Disease Neuroimaging Initiative. Projecting mri brain images for the detection of alzheimer’s disease. Stud. Health Technol. Inform. 207, 225–233 (2015)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Chetverikov, D., Peteri, R.: A brief survey of dynamic texture description and recognition. In: Proc. Intl. Conf. Computer Recognition Systems, pp. 17–26. Springer (2005)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Paulhac, L., Makris, P., Ramel, J.-Y.: Comparison between 2d and 3d local binary pattern methods for characterisation of three-dimensional textures. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 670–679. Springer, Heidelberg (2008)
Montagne, C., Kodewitz, A., Vigneron, V., Giraud, V., Lelandais, S.: 3D Local Binary Pattern for PET image classification by SVM, Application to early Alzheimer disease diagnosis. In: 6th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2013), Barcelona, Spain, pp. 145–150 (February 2013)
Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press (2007)
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
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Martinez-Murcia, F.J., Ortiz, A., Górriz, J.M., Ramírez, J., Illán, I.A. (2015). A Volumetric Radial LBP Projection of MRI Brain Images for the Diagnosis of Alzheimer’s Disease. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-18914-7_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18913-0
Online ISBN: 978-3-319-18914-7
eBook Packages: Computer ScienceComputer Science (R0)