Skip to main content
SpringerLink
Log in

Further Results on Alzheimer Disease Detection on Structural MRI Features

  • Conference paper
Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 87))

  • 1327 Accesses

Abstract

In this paper, we report further classification results of a feature extraction method from Structural Magnetic Resonance Imaging (sMRI) volumes for the detection of Alzheimer Disease (AD). The feature extraction process is based on the results of Voxel Based Morphometry (VBM) analysis of sMRI obtained from a set of patient and control subjects. We applied RVM classifier and compared the results with several neural network based algorithms trained and tested on these features. Results show well balanced sensitivity and specificity after 10-fold cross-validation, contrary to other classifiers that show some bias between them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ashburner, J., Friston, K.J.: Voxel-Based Morphometry–The methods. NeuroImage 11(6), 805–821 (2000)

    Article  Google Scholar 

  2. Bowd, C., Medeiros, F.A., Zhang, Z., Zangwill, L.M., Hao, J., Lee, T.-W., Sejnowski, T.J., Weinreb, R.N., Goldbaum, M.H.: Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Invest. Ophthalmol. Vis. Sci. 46(4), 1322–1329 (2005)

    Article  Google Scholar 

  3. Caesarendra, W., Widodo, A., Pham, H.T., Yang, B.-S.: Machine degradation prognostic based on RVM and ARMA/GARCH model for bearing fault simulated data. In: Prognostics and Health Management Conference, PHM 2010, pp. 1–6 (2010)

    Google Scholar 

  4. Chen, S., Gunn, S.R., Harris, C.J.: The relevance vector machine technique for channel equalization application. IEEE Transactions on Neural Networks 12(6), 1529–1532 (2001)

    Article  Google Scholar 

  5. Corchado, E., Arroyo, A., Tricio, V.: Soft computing models to identify typical meteorological days. Logic Journal of IGPL

    Google Scholar 

  6. Demir, B., Erturk, S.: Hyperspectral data classification using RVM with pre-segmentation and RANSAC. In: IEEE International on Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 1763–1766 (2007)

    Google Scholar 

  7. García Sebastián, M., Fernández, E., Graña, M., Torrealdea, F.J.: A parametric gradient descent MRI intensity inhomogeneity correction algorithm. Pattern Recogn. Lett. 28(13), 1657–1666 (2007)

    Article  Google Scholar 

  8. 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)

    Chapter  Google Scholar 

  9. Garcia-Sebastian, M., Hernandez, C., d’Anjou, A.: Robustness of an adaptive mri segmentation algorithm parametric intensity inhomogeneity modeling. Neurocomput. 72(10-12), 2146–2152 (2009)

    Article  Google Scholar 

  10. Graña, M.: A brief review of Lattice Computing. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008 (IEEE World Congress on Computational Intelligence), pp. 1777–1781 (June 2008)

    Google Scholar 

  11. Lima, C.A.M., Coelho, A.L.V., Chagas, S.: Automatic EEG signal classification for epilepsy diagnosis with relevance vector machines. Expert Systems with Applications 36(6), 10054–10059 (2009)

    Article  Google Scholar 

  12. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007) PMID: 17714011

    Google Scholar 

  13. Ozer, S., Haider, M.A., Langer, D.L., van der Kwast, T.H., Evans, A.J., Wernick, M.N., Trachtenberg, J., Yetik, I.S.: Prostate cancer localization with multispectral MRI based on relevance vector machines. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 73–76 (2009)

    Google Scholar 

  14. Savio, A., García-Sebastián, M., Graña, M., Villanúa, J.: Results of an adaboost approach on alzheimer’s disease detection on MRI. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 114–123. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  15. Savio, A., García-Sebastián, M., Hernández, C., Graña, M., Villanúa, J.: Classification results of artificial neural networks for alzheimer’s disease detection. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 641–648. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  16. Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)

    Google Scholar 

  17. Selvathi, D., Ram Prakash, R.S., Thamarai Selvi, S.: Performance evaluation of kernel based techniques for brain MRI data classification. In: International Conference on Conference on Computational Intelligence and Multimedia Applications, vol. 2, pp. 456–460 (2007)

    Google Scholar 

  18. Silva, C., Ribeiro, B.: Two-Level hierarchical hybrid SVM-RVM classification model. In: 5th International Conference on Machine Learning and Applications, ICMLA 2006, pp. 89–94 (2006)

    Google Scholar 

  19. Tashk, A.R.B., Sayadiyan, A., Valiollahzadeh, S.M.: Face detection using adaboosted RVM-based component classifier. In: 5th International Symposium on Image and Signal Processing and Analysis, ISPA 2007, pp. 351–355 (2007)

    Google Scholar 

  20. Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1(3), 211–244 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  21. Tipping, M.E., Faul, A., 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Grupo de Inteligencia Computacional, UPV/EHU, Spain

    Maite Termenon Conde & Manuel Graña

Authors
  1. Maite Termenon Conde
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VŠB-TU Ostrava, 17. listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. University of Burgos, Avenida Cantaria S/N, 09006, Burgos, Spain

    Javier Sedano

  4. Cairo University, 5 Ahmed Zewal St., Orman, Cairo, Egypt

    Aboul Ella Hassanien

  5. University of La Coruña, Avda. 19 de Febrero, S/N, A Coruña,, 15403, Ferrol, Spain

    José Luis Calvo

  6. Infobright, 47 Colborne Street, Suite 403, M5E1P8, Toronto, Ontario, Canada

    Dominik Ślȩzak

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Conde, M.T., Graña, M. (2011). Further Results on Alzheimer Disease Detection on Structural MRI Features. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19644-7_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19643-0

  • Online ISBN: 978-3-642-19644-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation