Abstract
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.
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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)
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)
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)
Seidman, L.J., et al.: Left hippocampal volume as a vulnerable indicator for schizophrenia. Arch. Gen. Psychiatry 59 (2002)
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)
Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Proc. SPIE Medical Imaging (2001)
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)
Jenkinson, M.: A bayesian similarity function for segmentation using anatomical, shape-based models. Technical report, FMRIB, University of Oxford (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Patenaude, B., Smith, S., Jenkinson, M. (2006). A Bayesian Cost Function Applied to Model-Based Registration of Sub-cortical Brain Structures. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_2
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DOI: https://doi.org/10.1007/11784012_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35648-6
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