May 28, 2005

Multi-modal image set registration and atlas formation

Medical Image Analysis
Peter LorenzenSarang Joshi

Abstract

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.

  • References11
  • Citations39

References

  • References11
  • Citations39

Citations

Mentioned in this Paper

Biologic Segmentation
Diagnostic Radiology Modality
Anatomic Structures
Subtraction Technique
Brain
Neuroanatomy
Gray Matter
Cerebrospinal Fluid
Image Interpretation, Computer-Assisted
Knowledge Representation (Computer)

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