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A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm.

Santhos Kumar Avuti1, Varun Bajaj1, Anil Kumar1

  • 11PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, 482005 India.

Biomedical Engineering Letters
|December 5, 2019
PubMed
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This study introduces a new method for segmenting pectoral muscles in mammograms using electro-magnetism optimization (EMO). This technique improves breast cancer detection accuracy by accurately isolating relevant image areas.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate breast cancer detection relies on excluding pectoral muscles from mammograms.
  • Pectoral muscle segmentation is crucial for reliable mammographic analysis.

Purpose of the Study:

  • To propose a novel multilevel thresholding technique for pectoral muscle segmentation in mammograms.
  • To enhance the accuracy of breast cancer detection by improving image segmentation.

Main Methods:

  • Developed a new multilevel thresholding method based on the electro-magnetism optimization (EMO) technique.
  • Integrated Kapur's and Otsu-based cost functions with the EMO algorithm.
  • Applied the methodology to the Mammographic Image Analysis Society (MIAS) dataset.
Keywords:
Computer aided diagnosis (CAD)Electro-magnetism optimization algorithm (EMO)Kapur’s and Otsu methodMammogram imagesMultilevel thresholdingPectoral muscle segmentation

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Main Results:

  • Achieved successful segmentation of pectoral muscles in a majority of mammograms from the MIAS dataset.
  • Demonstrated the robustness of the proposed algorithm across variations in pectoral muscle appearance.
  • Identified optimal threshold levels for mammographic image segmentation.

Conclusions:

  • The proposed EMO-based multilevel thresholding is an effective method for pectoral muscle segmentation.
  • This technique contributes to more accurate breast cancer detection through improved image analysis.
  • The algorithm shows promise for clinical application in mammography.