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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Quantitative Analysis

Background:

  • Accurate segmentation and volume calculation of breast areas in medical images are crucial for diagnosis and treatment monitoring.
  • Existing methods may lack automation and quantitative precision in analyzing complex breast tissues.

Purpose of the Study:

  • To develop and validate automated methods for defining breast contours and calculating volumes of interest in tomographic breast images.
  • To assess the reliability and potential of quantitative analysis for breast MRI data.

Main Methods:

  • Utilized the Breast-MRI-NACT-Pilot image collection for patient data.
  • Developed programs for threshold segmentation and watershed methods in MATLAB for image segmentation.
  • Implemented dynamic programming for algorithm adjustment and employed the Hurst exponent for tissue analysis.

Main Results:

  • A program for automatic determination of breast volume and pathology volume was successfully developed and tested.
  • The developed methods provided reliable data for 13 patients' breast MRI images.
  • Hurst exponent values below 0.4 indicated pathology, while values above 0.4 suggested healthy tissue.

Conclusions:

  • The developed contour and volume definition methods enable automatic quantitative evaluation of breast areas in post-processed MRI images.
  • The Hurst exponent shows promise as an additional tool for identifying pathologies in breast MRI scans.
  • Automated quantitative analysis enhances the assessment of breast tissue characteristics and abnormalities.