A Volumetric Radial LBP Projection of MRI Brain Images for the Diagnosis of Alzheimer’s Disease

  • F. J. Martinez-Murcia
  • A. Ortiz
  • J. M. Górriz
  • J. Ramírez
  • I. A. Illán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)

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.

Keywords

LBP SVM MRI Alzheimer’s Disease Projection 

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References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Ayache, N.: Analyzing 3D Images of the Brain. NeuroImage 4(3), S34–S35 (1996)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press (2007)Google Scholar
  19. 19.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • F. J. Martinez-Murcia
    • 1
  • A. Ortiz
    • 2
  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • I. A. Illán
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversidad of GranadaGranadaSpain
  2. 2.Department of Communications EngineeringUniversity of MálagaMálagaSpain

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