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Related Experiment Video

Updated: Apr 15, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Mammogram segmentation using maximal cell strength updation in cellular automata.

J Anitha1, J Dinesh Peter

  • 1Department of Computer Science and Engineering, Karunya University, Coimbatore, India.

Medical & Biological Engineering & Computing
|April 6, 2015
PubMed
Summary

This study introduces an automatic method for segmenting breast masses in mammograms using cellular automata (CA). The novel approach achieves high accuracy in identifying suspicious regions for early breast cancer detection.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Breast cancer is a leading cause of cancer diagnosis in women.
  • Mammography is crucial for early breast cancer detection.
  • Accurate segmentation of breast masses is vital for computer-aided diagnosis systems.

Purpose of the Study:

  • To develop an automatic segmentation method for identifying and segmenting suspicious breast mass regions in mammograms.
  • To improve the accuracy of computer-aided detection of breast cancer.

Main Methods:

  • A modified cellular automata (CA) transition rule, maximal cell strength updation, is proposed.
  • Coarse-level segmentation uses adaptive global thresholding based on histogram peak analysis.
  • Automatic seed point selection is performed using gray-level co-occurrence matrix features.
  • CA with the modified rule and selected seed points segments the mass region.

Main Results:

  • The proposed method was evaluated on 70 mammograms from the mini-MIAS database.
  • Achieved a sensitivity of 92.25% for mass region segmentation.
  • Demonstrated an accuracy of 93.48% in segmenting mass regions.

Conclusions:

  • The developed automatic segmentation approach shows promising results for breast mass detection in mammograms.
  • The novel CA-based method enhances the accuracy of computer-aided diagnosis for breast cancer.
  • This technique contributes to more effective early detection of breast cancer through improved image analysis.