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Updated: May 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A hybrid technique for medical image segmentation.

Alamgir Nyma1, Myeongsu Kang, Yung-Keun Kwon

  • 1School of Electrical Engineering, University of Ulsan, Building 7, Room No. 308, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea.

Journal of Biomedicine & Biotechnology
|August 25, 2012
PubMed
Summary

This study introduces a hybrid method for magnetic resonance (MR) image segmentation, improving accuracy in noisy conditions. The novel approach enhances computer-aided diagnosis and pattern recognition through robust brain MR image segmentation.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Pattern Recognition

Background:

  • Medical image segmentation is crucial but challenging for computer-aided diagnosis.
  • Magnetic resonance (MR) images often contain impulsive noise, hindering accurate segmentation.
  • Existing methods may struggle with noise robustness and segmentation precision.

Purpose of the Study:

  • To propose a hybrid method for accurate magnetic resonance (MR) image segmentation.
  • To enhance the robustness of segmentation algorithms in the presence of various noise types and levels.
  • To improve the performance of fuzzy c-means (FCM) based segmentation for brain MR images.

Main Methods:

  • A hybrid approach combining a vector median filter for noise removal, Otsu thresholding for initial segmentation, and an enhanced suppressed fuzzy c-means (ESFCM) algorithm.

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Hybrid µCT-FMT imaging and image analysis
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Hybrid µCT-FMT imaging and image analysis
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Published on: June 4, 2015

  • The ESFCM algorithm utilizes an optimal suppression factor for precise data clustering.
  • Evaluation involved adding diverse noise types and amounts to T1-weighted brain MR images.

    • Main Results:

    • The proposed hybrid method demonstrated superior segmentation accuracy compared to other FCM-based algorithms.
    • The algorithm showed significant robustness when applied to both noise-free and noise-inserted MR images.
    • Experimental results validate the effectiveness of the vector median filter, Otsu thresholding, and ESFCM combination.

    Conclusions:

    • The developed hybrid method offers a robust and accurate solution for magnetic resonance (MR) image segmentation.
    • This approach holds promise for advancing computer-aided diagnosis and pattern recognition applications.
    • The enhanced suppressed fuzzy c-means algorithm provides improved clustering performance in noisy medical image data.