Partial Volume Estimation in Brain MRI Revisited

  • Alexis Roche
  • Florence Forbes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


We propose a fast algorithm to estimate brain tissue concentrations from conventional T1-weighted images based on a Bayesian maximum a posteriori formulation that extends the “mixel” model developed in the 90’s. A key observation is the necessity to incorporate additional prior constraints to the “mixel” model for the estimation of plausible concentration maps. Experiments on the ADNI standardized dataset show that global and local brain atrophy measures from the proposed algorithm yield enhanced diagnosis testing value than with several widely used soft tissue labeling methods.


Hellinger Distance Histogram Mode Partial Volume Estimation Normalize Hippocampus Volume Additional Prior Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)CrossRefGoogle Scholar
  2. 2.
    Ashburner, J., Friston, K.: Unified segmentation. NeuroImage 26(3), 839–851 (2005)CrossRefGoogle Scholar
  3. 3.
    Choi, H.S., Haynor, D.R., Kim, Y.: Partial Volume Tissue Classification of Multichannel Magnetic Resonance Images – A Mixel Model. IEEE Transactions on Medical Imaging 10(3), 395–407 (1991)CrossRefGoogle Scholar
  4. 4.
    Nocera, L., Gee, J.C.: Robust partial volume tissue classification of cerebral MRI scans. In: SPIE Medical Imaging. vol. 3034, pp. 312–322. SPIE (1997)Google Scholar
  5. 5.
    Laidlaw, D.H., Fleischer, K.W., Barr, A.H.: Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging 17(1), 74–86 (1998)CrossRefGoogle Scholar
  6. 6.
    Pham, D.L., Prince, J.L.: Unsupervised partial volume estimation in single-channel image data. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), pp. 170–177. IEEE (2000)Google Scholar
  7. 7.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: A Unifying Framework for Partial Volume Segmentation of Brain MR Images. IEEE Transactions on Medical Imaging 22(1), 105–119 (2003)CrossRefGoogle Scholar
  8. 8.
    Liang, Z., Wang, S.: An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis. IEEE Transactions on Medical Imaging 28(2), 297–310 (2009)CrossRefGoogle Scholar
  9. 9.
    Duché, Q., Acosta, O., Gambarota, G., Merlet, I., Salvado, O., Saint-Jalmes, H.: Bi-exponential magnetic resonance signal model for partial volume computation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 231–238. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Roche, A., Ribes, D., Bach-Cuadra, M., Krueger, G.: On the Convergence of EM-Like Algorithms for Image Segmentation using Markov Random Fields. Medical Image Analysis 15(6), 830–839 (2011)CrossRefGoogle Scholar
  11. 11.
    Brandt, M.E., Bohant, T.P., Kramer, L.A., Fletcher, J.M.: Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Computerized Medical Imaging and Graphics 18(1), 25–34 (1994)CrossRefGoogle Scholar
  12. 12.
    Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18(9), 737–752 (1999)CrossRefGoogle Scholar
  13. 13.
    Grady, L.: Random Walks for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  14. 14.
    Shattuck, D., Sandor-Leahy, S., Schaper, K., Rottenberg, D., Leahy, R.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5), 856–876 (2001)CrossRefGoogle Scholar
  15. 15.
    Bach Cuadra, M., Cammoun, L., Butz, T., Cuisenaire, O., Thiran, J.P.: Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Transactions on Medical Imaging 24(12), 1548–1565 (2005)CrossRefGoogle Scholar
  16. 16.
    Gudbjartsson, H., Patz, S.: The Rician Distribution of Noisy MRI Data. Magnetic Resonance in Medicine 34(6), 910–914 (1995)CrossRefGoogle Scholar
  17. 17.
    Larsson, E.G., Erdogmus, D., Yan, R., Principe, J.C., Fitzsimmons, J.R.: SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging. Journal of Magnetic Resonance 163, 121–123 (2003)CrossRefGoogle Scholar
  18. 18.
    Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (1999)CrossRefzbMATHGoogle Scholar
  19. 19.
    Dauguet, J., Mangin, J.-F., Delzescaux, T., Frouin, V.: Robust inter-slice intensity normalization using histogram scale-space analysis. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 242–249. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Kwan, R.S., Evans, A., Pike, G.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18(11), 1085–1097 (1999)CrossRefGoogle Scholar
  21. 21.
    González Ballester, M.A., Zisserman, A.P., Brady, M.: Estimation of the partial volume effect in MRI. Medical Image Analysis 6, 389–405 (2002)CrossRefGoogle Scholar
  22. 22.
    Wyman, B., Harvey, D., Crawford, K., Bernstein, M., Carmichael, O., Cole, P., Crane, P., Decarli, C., Fox, N., Gunter, J., Hill, D., Killiany, R., Pachai, C., Schwarz, A., Schuff, N., Senjem, M., Suhy, J., Thompson, P., Weiner, M., Jack, C.: the Alzheimer’s Disease Neuroimaging Initiative: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s & Dementia (2012)Google Scholar
  23. 23.
    Chefd’hotel, C., Hermosillo, G., Faugeras, O.: Flows of diffeomorphisms for multimodal image registration. In: Proc. IEEE International Symposium on Biomedical Imaging, pp. 753–756 (2002)Google Scholar
  24. 24.
    Bostanci, B., Bostanci, E.: An evaluation of classification algorithms using Mc Nemar’s test. In: Bansal, J.C., Singh, P.K., Deep, K., Pant, M., Nagar, A.K. (eds.) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). AISC, vol. 201, pp. 15–26. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  25. 25.
    Ribes, D., Mortamet, B., Bach-Cuadra, M., Jack, C., Meuli, R., Krueger, G., Roche, A.: Comparison of tissue classification models for automatic brain MR segmentation. In: ISMRM, Montreal, Canada (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexis Roche
    • 1
    • 2
    • 3
  • Florence Forbes
    • 4
    • 5
  1. 1.Siemens Advanced Clinical Imaging TechnologyLausanneSwitzerland
  2. 2.Department of RadiologyCHUVLausanneSwitzerland
  3. 3.Signal Processing Laboratory (LTS5)EPFLLausanneSwitzerland
  4. 4.Mistis ProjectINRIAGrenobleFrance
  5. 5.Laboratoire Jean KuntzmannGrenoble UniversityGrenobleFrance

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