A Bayesian Cost Function Applied to Model-Based Registration of Sub-cortical Brain Structures

  • Brian Patenaude
  • Stephen Smith
  • Mark Jenkinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)


Morphometric analysis and anatomical correspondence across MR images is important in understanding neurological diseases as well as brain function. By registering shape models to unseen data, we will be able to segment the brain into its sub-cortical regions. A Bayesian cost function was derived for this purpose and serves to minimize the residuals to a planar intensity model. The aim of this paper is to explore the properties and justify the use of the cost function. In addition to a pure residual term (similar to correlation ratio) there are three additional terms, one of which is a growth term. We show the benefit of incorporating an additional growth term into a purely residual cost function. The growth term minimizes the size of the structure in areas of high residual variance. We further show the cost function’s dependence on the local intensity contrast estimate for a given structure.


Cost Function Growth Term Active Shape Model Correlation Ratio Statistical Shape Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Collins, D., Neelin, P., Peters, T., Evans, A.: Automatic 3d intersubject registration of mr volumetric data in standardized talairach space. Journal of Computer Assisted Tomography 18, 192–205 (1994)CrossRefGoogle Scholar
  2. 2.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)CrossRefGoogle Scholar
  3. 3.
    Kennedy, D.N., Lange, N., Makris, N., Bates, J., Meyer, J., Caviness, J.V.S.: Gyri of the human neocortex: An mri-based analysis of volume and variance. Cerebral Cortex 8, 372–384 (1998)CrossRefGoogle Scholar
  4. 4.
    Seidman, L.J., et al.: Left hippocampal volume as a vulnerable indicator for schizophrenia. Arch. Gen. Psychiatry 59 (2002)Google Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)CrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Proc. SPIE Medical Imaging (2001)Google Scholar
  7. 7.
    Jenkinson, M., Behrens, T., Woolrich, M., Crum, W., Hill, D., Smith, S.: A bayesian similarity function for segmentation using anatomical, shape-based models. In: Medical Image Understanding and Analysis (MIUA) (2005)Google Scholar
  8. 8.
    Jenkinson, M.: A bayesian similarity function for segmentation using anatomical, shape-based models. Technical report, FMRIB, University of Oxford (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Brian Patenaude
    • 1
  • Stephen Smith
    • 1
  • Mark Jenkinson
    • 1
  1. 1.FMRIB CentreUniversity of Oxford 

Personalised recommendations