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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Abnormality segmentation in brain images via distributed estimation.

Evangelia I Zacharaki1, Anastasios Bezerianos

  • 1School of Medicine, University of Patras, Rio 26504, Achaia, Greece. ezachar@upatras.gr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|December 14, 2011
PubMed
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This study introduces a novel semisupervised learning method for automated medical image analysis. The technique improves abnormality detection and segmentation, outperforming existing methods like Statistical Parametric Mapping.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Semisupervised learning offers automated medical image analysis without pathology modeling.
  • High dimensionality poses challenges for traditional probability density estimation in abnormality detection.
  • Existing methods like Statistical Parametric Mapping (SPM) may have limitations in image segmentation.

Purpose of the Study:

  • To introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images.
  • To address the challenge of high dimensionality in probability density estimation for anomaly detection.
  • To enhance the automation and accuracy of medical image analysis.

Main Methods:

  • Treating images as networks of locally coherent partitions (overlapping blocks).

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  • Formulating and maximizing a strictly concave likelihood function for abnormality estimation per partition.
  • Employing a distributed estimation algorithm to fuse local estimates into a globally optimal solution.
  • Utilizing quadratic programming for the likelihood function, incorporating model and data terms.

    • Main Results:

    • Successfully applied to segment brain pathologies like simulated infarction and dysplasia.
    • Demonstrated effectiveness on real lesions in diabetes patients.
    • Receiver operating characteristic (ROC) analysis showed improved image segmentation compared to two-group analysis with SPM.

    Conclusions:

    • The proposed semisupervised scheme provides a robust and automated approach for medical image abnormality detection and segmentation.
    • The method overcomes dimensionality challenges through image partitioning and distributed estimation.
    • Achieved superior performance in segmentation accuracy compared to established methods.