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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Dynamic graph cut based segmentation of mammogram.

S Pitchumani Angayarkanni1, Nadira Banu Kamal2, Ranjit Jeba Thangaiya3

  • 1Department of Computer Science, Lady Doak College, Madurai, Tamil Nadu India.

Springerplus
|November 7, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic graph cut Otsu method for segmenting breast cancer masses in mammograms. The novel approach significantly improves detection accuracy, aiding early cancer diagnosis.

Keywords:
FuzzificationGraph cutOtsu’s method and ROC

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Breast cancer is a leading cause of death among women globally, particularly in India.
  • Mammography is crucial for early cancer detection, but computer-aided diagnosis (CAD) systems often lack accuracy and efficiency.
  • Accurate segmentation of cancerous masses in mammograms is vital for effective computer-aided diagnosis.

Purpose of the Study:

  • To present a dynamic graph cut-based Otsu's method for improved segmentation of masses in mammogram images.
  • To enhance the accuracy and efficiency of breast cancer detection through advanced image segmentation.
  • To evaluate the performance of the proposed segmentation algorithm against existing methods.

Main Methods:

  • Implementation of a dynamic graph cut algorithm integrated with Otsu's method for image segmentation.
  • Segmentation of the region of interest (ROI) containing potential cancerous masses.
  • Determination and comparison of key performance metrics: sensitivity, specificity, positive predictive value, and negative predictive value.

Main Results:

  • The proposed dynamic graph cut-based Otsu's method achieved high accuracy in segmenting masses.
  • Sensitivity: 98.88%, Specificity: 98.89%, Positive Predictive Value: 93%, Negative Predictive Value: 97.5%.
  • Qualitative and quantitative analyses confirmed the superior performance compared to existing algorithms.

Conclusions:

  • The dynamic graph cut-based Otsu's method offers a highly accurate and effective solution for segmenting cancerous masses in mammograms.
  • This advancement holds significant potential for improving the reliability and efficiency of early breast cancer diagnosis.
  • The proposed algorithm demonstrates superior performance metrics, paving the way for more robust computer-aided diagnosis systems.