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A mean shift based fuzzy c-means algorithm for image segmentation.

Huiyu Zhou1, Gerald Schaefer, Chunmei Shi

  • 1School of Engineering and Design, Brunel University, U.K. Huiyu.Zhou@brunel.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
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This study introduces a faster fuzzy c-means algorithm using mean shift for medical image segmentation. The new method improves computational efficiency while maintaining high-quality image segmentation performance.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Image segmentation is crucial for medical applications.
  • Clustering algorithms, like fuzzy c-means, are used for segmentation.
  • Traditional fuzzy c-means is computationally intensive due to its iterative nature.

Purpose of the Study:

  • To develop a faster and efficient fuzzy c-means algorithm for image segmentation.
  • To improve segmentation performance in medical imaging applications.

Main Methods:

  • Introduced a novel mean shift-based fuzzy c-means algorithm.
  • Incorporated a mean field term into the standard fuzzy c-means objective function.
  • Utilized mean shift's ability to rapidly identify cluster centers.

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Main Results:

  • The proposed algorithm demonstrates faster computation compared to previous methods.
  • Achieved good segmentation performance, effectively segmenting clusters within images.
  • The integration of mean shift enhances the efficiency of fuzzy c-means.

Conclusions:

  • The new mean shift-based fuzzy c-means algorithm offers a computationally efficient solution for medical image segmentation.
  • This approach provides a balance between speed and accuracy in image segmentation tasks.
  • The method is capable of optimal cluster segmentation within medical images.