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Segmentation of Brain MRI Images using Multi-Kernel FCM EHO Method

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  • 1Department of ECE, School of Engineering, SR University, Warangal-506371, Telangana, India.

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This study introduces an effective new method for segmenting brain tumors in MRI scans. The proposed technique improves accuracy in identifying tumorous regions compared to traditional approaches.

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Contrast enhancementMulti-kernel fuzzy c-means clustering.OptimizationPartial differential equationSegmentationThresholding

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Image segmentation is challenging due to variations in shape, location, and intensity.
  • Brain tumor detection and segmentation are critical in medical diagnostics.
  • Accurate segmentation aids in treatment planning and patient outcomes.

Purpose of the Study:

  • To segment brain Magnetic Resonance Imaging (MRI) images into tumor and non-tumor regions.
  • To develop an automated methodology for precise brain tumor segmentation.
  • To enhance the visibility and delineation of brain tumors in medical images.

Main Methods:

  • Utilized MRI images from the BraTS2020 database.
  • Applied contrast enhancement using thresholding.
  • Implemented image denoising with a fourth-order partial differential equation.
  • Employed an elephant herding algorithm for centroid optimization.
  • Performed image segmentation using multi-kernel fuzzy c-means clustering.

Main Results:

  • Performance evaluated using Peak Signal-to-Noise Ratio, Mean Square Error, sensitivity, specificity, and accuracy.
  • The proposed method demonstrated superior performance compared to conventional techniques.
  • Quantitative metrics indicate enhanced accuracy in tumor segmentation.

Conclusions:

  • The developed methodology is a more effective technique for brain tumor segmentation.
  • The proposed approach offers improved accuracy and reliability over existing methods.
  • This technique shows promise for clinical application in brain tumor analysis.