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Related Experiment Video

Updated: Mar 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Breast mass classification on mammograms using radial local ternary patterns.

Chisako Muramatsu1, Takeshi Hara1, Tokiko Endo2

  • 1Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

Computers in Biology and Medicine
|March 26, 2016
PubMed
Summary
This summary is machine-generated.

Radial local ternary patterns (RLTP) effectively differentiate benign and malignant breast masses on mammograms. This new texture analysis method outperforms other techniques, improving diagnostic accuracy without precise lesion segmentation.

Keywords:
Breast massesClassificationLocal binary patternsLocal ternary patternsMammogramsTexture feature

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Mammography is crucial for breast cancer detection.
  • Accurate differentiation between benign and malignant lesions is essential.
  • Traditional methods rely on shape/margin features requiring manual segmentation, which can be labor-intensive and error-prone.

Purpose of the Study:

  • To investigate radial local ternary patterns (RLTP) as an region of interest (ROI)-based texture feature for classifying benign and malignant breast masses.
  • To compare the performance of RLTP with other texture analysis methods, including regular local ternary patterns (LTP), rotation invariant uniform (RIU2) LTP, gray level co-occurrence matrix (GLCM), and wavelet features.

Main Methods:

  • Utilized 376 ROIs from mammograms (181 malignant, 195 benign).
  • Extracted texture features using RLTP, LTP, RIU2-LTP, GLCM, and wavelet methods.
  • Classified ROIs using artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers.

Main Results:

  • RLTP achieved the highest area under the receiver operating characteristic curve (AUC) values across all classifiers, reaching 0.90 with the ANN.
  • RLTP demonstrated superior performance compared to LTP (AUC=0.77), RIU2-LTP (AUC=0.78), GLCM (AUC=0.86), and wavelet features (AUC=0.83).

Conclusions:

  • RLTP is a valuable texture feature for distinguishing benign from malignant breast lesions on mammograms.
  • The proposed radial pattern analysis shows superiority over conventional rotation-invariant texture patterns for this classification task.