Physical model-based non-rigid registration incorporating statistical shape information

Yongmei Wang, Lawrence H. Staib

Research output: Contribution to journalArticlepeer-review


This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second, to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, an intensity similarity measure, and statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model-based non-rigid registration and the two methods we present each have advantages under different conditions.

Original languageEnglish (US)
Pages (from-to)7-20
Number of pages14
JournalMedical Image Analysis
Issue number1
StatePublished - 2000
Externally publishedYes


  • Bayesian formulation
  • Deformable atlases
  • Non-rigid registration
  • Physical models
  • Statistical shape models

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Medicine (miscellaneous)
  • Computer Science (miscellaneous)


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