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3D cerebral MR image segmentation using multiple-classifier system.

Saba Amiri1, Mohammad Mehdi Movahedi2,3, Kamran Kazemi4

  • 1Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran.

Medical & Biological Engineering & Computing
|May 22, 2016
PubMed
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This summary is machine-generated.

Accurate brain tissue segmentation using a novel multiple-classifier system improves accuracy for white matter, gray matter, and cerebrospinal fluid analysis in MRI scans.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence

Background:

  • Accurate segmentation of brain tissues like white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI is crucial for clinical applications.
  • Existing segmentation methods may lack the required accuracy for precise neuroimaging analysis, diagnosis, and surgical planning.

Purpose of the Study:

  • To develop and evaluate a multiple-classifier system for automatic and accurate brain tissue segmentation from cerebral MRI.
  • To enhance the precision of WM, GM, and CSF segmentation compared to single classifier approaches and existing tools.

Main Methods:

  • A system combining preprocessing (intensity correction, skull stripping), feature extraction (statistical and non-statistical), and supervised classification was developed.
Keywords:
BrainImage segmentationMRIMultilayer perceptionMultiple-classifier systemNeural network

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  • Three multilayer perceptron (MLP) neural networks served as base classifiers, with their outputs fused via majority voting.
  • The system was evaluated using simulated (Brainweb) and real (IBSR) MRI datasets.

    • Main Results:

    • The proposed multiple-classifier system demonstrated improved segmentation accuracy, particularly for CSF (around 5%).
    • Quantitative assessments using Dice, Jaccard, and conformity coefficient metrics showed superiority over single MLP classifiers.
    • The system outperformed existing tools like FSL-FAST and SPM in accuracy.

    Conclusions:

    • The multiple-classifier approach offers a significant improvement in brain MRI segmentation accuracy.
    • This enhanced accuracy is vital for advancing the clinical utility of MRI segmentation techniques in diagnostics and treatment planning.