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Magnetic resonance image segmentation using pattern recognition, and applied to image registration and quantitation

N Saeed1

  • 1MRI Unit, Hammersmith Hospital, London, UK.

NMR in Biomedicine
|August 27, 1998
PubMed
Summary

This review explores pattern recognition in magnetic resonance image (MRI) segmentation algorithms. It covers low- to high-level processing for anatomical identification and its applications in disease monitoring and volume measurement.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for non-invasive anatomical visualization.
  • Accurate segmentation of anatomical structures in MRI is essential for quantitative analysis.
  • Existing segmentation methods vary in complexity and application.

Purpose of the Study:

  • To review and categorize pattern recognition-based MRI segmentation algorithms.
  • To examine the role of different image processing levels in anatomical identification.
  • To highlight the clinical applications of segmented MRI data.

Main Methods:

  • Categorization of algorithms into low- to intermediate-level and high-level processing.
  • Analysis of techniques including histogram analysis, texture definition, edge identification, region growing, and contour following.

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  • Examination of prior knowledge, neural networks, and cluster analysis for objective identification.

    • Main Results:

    • Segmentation algorithms are classified based on their processing approach.
    • Various techniques contribute to objective identification of anatomical structures.
    • Segmented MRI data facilitates image registration, disease progression monitoring, and volume measurement.

    Conclusions:

    • Pattern recognition plays a vital role in diverse MRI segmentation techniques.
    • Accurate segmentation enables critical applications in clinical research and patient care.
    • The review provides a framework for understanding and applying MRI segmentation methods.