Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

Ashraf Mohamed, Evangelia I. Zacharaki, Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Original languageEnglish (US)
Pages (from-to)752-763
Number of pages12
JournalMedical image analysis
Issue number5
StatePublished - Oct 2006
Externally publishedYes


  • Atlas registration
  • Brain image registration
  • Brain tumor
  • Finite element model
  • Neurosurgical planning
  • Statistical deformation model

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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