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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance

Vibha Bafna Bora1, Ashwin G Kothari2, Avinash G Keskar3

  • 1Department of Electronics and Telecommunication Engineering, G. H. Raisoni College of Engineering, CRPF Gate No. 3 Hingna Road, Nagpur, 440016, Maharashtra, India. vibha.bora@raisoni.net.

Journal of Digital Imaging
|August 12, 2015
PubMed
Summary
This summary is machine-generated.

Accurate pectoral muscle segmentation in mammograms is crucial for computer-aided diagnosis (CAD). This study introduces a novel texture gradient approach that effectively excludes pectoral muscle, improving CAD accuracy.

Keywords:
Computer aided diagnosisEuclidean distance regressionHough transformMammogramsPectoral muscle detectionTexture gradient

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate pectoral muscle segmentation is vital for reliable computer-aided diagnosis (CAD) in mammography.
  • The presence of pectoral muscle can introduce bias in CAD systems, affecting diagnostic accuracy.
  • Existing methods struggle with variations in tissue texture and overlapping glandular tissues.

Purpose of the Study:

  • To develop a novel, robust, and automatic method for pectoral muscle segmentation in mammograms.
  • To improve the accuracy of tissue segmentation in mediolateral oblique (MLO) view mammograms for CAD applications.
  • To provide a reliable approach for excluding pectoral muscle that is robust to various image characteristics.

Main Methods:

  • A texture gradient-based approach utilizing Probable Texture Gradient (PTG) maps.
  • Hough transform for initial pectoral edge approximation followed by block averaging.
  • Euclidean Distance Regression (EDR) technique and polynomial modeling for smooth curve generation.

Main Results:

  • Successfully segmented pectoral muscles in 96.75% of 340 MLO view mammograms across diverse databases.
  • Demonstrated robustness against varying textures and overlapping fibro glandular tissues.
  • Outperformed existing state-of-the-art methods in terms of accuracy and quantification of the pectoral edge.

Conclusions:

  • The proposed texture gradient-based method offers a highly accurate and efficient solution for pectoral muscle segmentation in MLO mammograms.
  • This technique significantly enhances the reliability and suitability of CAD systems by accurately excluding pectoral muscle.
  • The method's robustness and performance justify its implementation in clinical computer-aided diagnosis workflows.