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Pectoral muscle identification in mammograms.

K Santle Camilus1, V K Govindan, P S Sathidevi

  • 1Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, India. camilus@nitc.ac.in

Journal of Applied Clinical Medical Physics
|August 17, 2011
PubMed
Summary

This study introduces an automatic method using watershed transformation to accurately identify pectoral muscle in mammograms. The new approach improves cancer detection by precisely segmenting this muscle, outperforming existing methods.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Processing

Background:

  • Pectoral muscle removal is crucial for accurate mammogram analysis in computer-aided detection (CAD) of breast cancer.
  • Its presence can negatively impact cancer detection outcomes.
  • Existing methods often struggle with precise pectoral muscle segmentation.

Purpose of the Study:

  • To propose an efficient and automatic method for pectoral muscle identification in mediolateral oblique (MLO) view mammograms.
  • To improve the preprocessing stage of breast cancer detection systems.

Main Methods:

  • Utilizing watershed transformation to identify the pectoral muscle edge.
  • Developing a merging algorithm to correct oversegmentation caused by multiple catchment basins.
  • Validating the approach on 84 mammograms from the mammographic image analysis database.

Main Results:

  • Achieved a mean false positive rate of 0.85% and a mean false negative rate of 4.88% compared to ground truth.
  • Demonstrated superior performance over state-of-the-art methods, particularly in reducing the mean false negative rate.
  • Showcased low Hausdorff distances (mean ± std dev: 3.85 ± 1.07 mm) when compared to manual segmentations.

Conclusions:

  • The proposed watershed transformation-based method offers an efficient and accurate solution for pectoral muscle segmentation in mammograms.
  • This automated approach enhances the reliability of computer-aided detection systems for breast cancer.
  • The method's improved accuracy, especially in reducing false negatives, holds significant potential for clinical application.