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Related Experiment Video

Updated: Feb 12, 2026

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.

Rongbo Shen1, Kezhou Yan2, Fen Xiao2

  • 1Key Laboratory of Information Storage System, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, People's Republic of China. rock_shen@hust.edu.cn.

Journal of Digital Imaging
|March 28, 2018
PubMed
Summary
This summary is machine-generated.

Accurate pectoral muscle segmentation in mammograms is vital for computer-aided diagnosis. This study introduces a novel method combining genetic and morphological algorithms, significantly reducing false positives and improving lesion detection in breast cancer screening.

Keywords:
Breast mammographyGenetic algorithmMorphological selectionPectoral muscle region segmentation

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Pectoral muscle segmentation in mammography is critical for accurate breast cancer diagnosis.
  • The pectoral muscle's similar texture and low contrast with breast tissue often lead to high false positive rates and misdiagnosis.
  • Accurate segmentation of this region, especially in poor-contrast mammograms, remains a significant challenge in medical image analysis.

Purpose of the Study:

  • To propose a novel, automated method for segmenting the pectoral muscle region in mammograms.
  • To enhance the accuracy of computer-aided diagnosis systems by improving pectoral muscle segmentation.
  • To reduce false positive rates and misdiagnoses caused by pectoral muscle interference.

Main Methods:

  • A novel method combining a genetic algorithm and a morphological selection algorithm for pectoral muscle segmentation.
  • The method incorporates four key steps: pre-processing, genetic algorithm application, morphological selection, and polynomial curve fitting.
  • Automated segmentation process designed to handle challenging poor-contrast mammograms.

Main Results:

  • Achieved competitive performance across multiple benchmark mammography databases (mini MIAS, DDSM, INBreast).
  • Demonstrated low average false positive (FP) rates: 2.03% (mini MIAS), 1.60% (DDSM), and 2.42% (INBreast).
  • Reported low average false negative (FN) rates: 6.90% (mini MIAS), 4.03% (DDSM), and 13.61% (INBreast).

Conclusions:

  • The proposed combined genetic and morphological algorithm offers an effective solution for automated pectoral muscle segmentation in mammography.
  • The method demonstrates comparable or superior performance to state-of-the-art techniques across various evaluation metrics.
  • This approach has the potential to significantly improve the reliability and accuracy of computer-aided diagnosis systems for breast cancer detection.