Adaptive Segmentation of MRI Data

  • W. M. WellsIII
  • W. E. L. Grimson
  • R. Kikinis
  • F. A. Jolesz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)


Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.


Bias Field Intensity Inhomogeneity Tissue Class Tissue Classification Adaptive Segmentation 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • W. M. WellsIII
    • 1
    • 2
  • W. E. L. Grimson
    • 2
  • R. Kikinis
    • 1
  • F. A. Jolesz
    • 1
  1. 1.Department of RadiologyHarvard Medical School and Brigham and Women’s HospitalBostonUSA
  2. 2.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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