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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Joberth de Nazaré Silva1, Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva

  • 1Applied Computing Group - NCA/UFMA, Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil, joberth1@yahoo.com.br.

Journal of Digital Imaging
|October 4, 2014
PubMed
Summary

This study introduces an automated method for breast cancer mass detection in mammograms using quality thresholding and support vector machines (SVM). The system achieved high accuracy, aiding early diagnosis and improving patient survival rates.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Breast cancer is a leading cause of cancer mortality worldwide.
  • Early detection significantly improves patient survival rates.
  • Computer-aided detection systems assist radiologists in identifying subtle abnormalities.

Purpose of the Study:

  • To develop and evaluate an automated method for detecting masses in digital mammograms.
  • To enhance early breast cancer diagnosis through improved detection accuracy.
  • To provide a tool for specialists to anticipate mass detection.

Main Methods:

  • Image preprocessing using low-pass filtering and wavelet transform enhancement.
  • Mass segmentation utilizing a quality threshold (QT) method.
  • Feature extraction with Haralick descriptors and correlogram functions.
  • Classification of mass candidates using a support vector machine (SVM).

Main Results:

  • Achieved a sensitivity of 92.31%, specificity of 82.2%, and accuracy of 83.53%.
  • Reported a mean false positive rate of 1.12 per image.
  • Obtained an area under the ROC curve of 0.8033.

Conclusions:

  • The proposed automated method effectively detects masses in digital mammograms.
  • The system demonstrates potential as a valuable tool for early breast cancer diagnosis.
  • Improved detection accuracy contributes to increased patient survival chances.