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Related Experiment Video

Updated: Jun 23, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.

Karan Sikka1, Nitesh Sinha, Pankaj K Singh

  • 1Department of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, India. k.sikka@iitg.ernet.in

Magnetic Resonance Imaging
|April 28, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces an automated brain MRI segmentation algorithm that improves accuracy by reducing noise and enhancing tissue boundaries. The novel method offers faster computation and convergence for better brain image analysis.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Automated brain magnetic resonance image (MRI) segmentation is challenging due to image quality issues like intensity inhomogeneity and noise.
  • Accurate segmentation is crucial for diagnosing and monitoring various brain conditions.

Purpose of the Study:

  • To develop a novel, fully automated algorithm for segmenting normal and diseased brain MRI scans.
  • To enhance the accuracy and efficiency of brain MRI segmentation compared to existing methods.

Main Methods:

  • An entropy-driven homomorphic filtering technique was used for bias field removal.
  • A histogram-based local peak merger with an adaptive window estimated initial cluster centers.
  • A modified fuzzy c-mean (MFCM) technique incorporating neighborhood pixel information was applied.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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Last Updated: Jun 23, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

  • A novel neighborhood-based membership ambiguity correction (NMAC) method smoothed boundaries and removed noise.

    • Main Results:

    • The proposed algorithm demonstrated superior segmentation results compared to MFCM and the FMRIB Software Library.
    • NMAC effectively sharpened tissue boundaries, improving the delineation of tissue and tumor areas.
    • The algorithm achieved fully automatic segmentation with faster computation and objective function convergence.

    Conclusions:

    • The developed algorithm provides a robust and efficient solution for automated brain MRI segmentation.
    • The combination of bias field correction, MFCM, and NMAC significantly enhances segmentation accuracy and boundary definition.
    • This method holds promise for improved clinical applications in brain image analysis and diagnostics.