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Adaptive segmentation of MRI data.

W M Wells1, W L Grimson, R Kikinis

  • 1Dept. of Radiol., Brigham & Women's Hospital, Boston, MA.

IEEE Transactions on Medical Imaging
|January 1, 1996
PubMed
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This study introduces adaptive segmentation, a new method for correcting and segmenting magnetic resonance imaging (MRI) brain scans. It accurately segments tissue types, improving visualization and accuracy compared to existing methods.

Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Computer Vision

Background:

  • Intensity-based classification of MR images is challenging due to intrascan and interscan intensity inhomogeneities.
  • Existing methods for correcting inhomogeneities often require manual supervision for each scan.
  • Accurate segmentation is crucial for quantitative analysis and visualization of brain structures.

Purpose of the Study:

  • To introduce and evaluate a novel adaptive segmentation method for MR images.
  • To address the limitations of current intensity-based classification and inhomogeneity correction techniques.
  • To improve the accuracy and visualization of brain tissue segmentation in MRI.

Main Methods:

  • Developed an adaptive segmentation method utilizing knowledge of tissue intensity properties and inhomogeneities.

Related Experiment Videos

  • Employed the expectation-maximization (EM) algorithm for image correction and segmentation.
  • Validated the method on over 1000 brain scans across various MRI sequences and coils.

    • Main Results:

    • The adaptive segmentation method demonstrated accurate segmentation of brain tissues, including gray and white matter.
    • Results showed comparable accuracy to manual segmentation.
    • The method outperformed supervised multivariant classification in terms of closeness to manual segmentation.

    Conclusions:

    • Adaptive segmentation offers a robust and accurate approach for MR image analysis.
    • The EM-based method effectively corrects inhomogeneities and enhances tissue segmentation.
    • This technique shows significant potential for improving neuroimaging research and clinical applications.