Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography

Chung Ming Lo, Yeun Chung Chang, Ya Wen Yang, Chiun Sheng Huang, Ruey Feng Chang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Background: Elastography is a new sonographic imaging technique to acquire the strain information of tissues and transform the information into images. Radiologists have to observe the gray-scale distribution of tissues on the elastographic image interpreted as the reciprocal of Young[U+05F3]s modulus to evaluate the pathological changes such as scirrhous carcinoma. In this study, a computer-aided diagnosis (CAD) system was developed to extract quantitative strain features from elastographic images to reduce operator-dependence and provide an automatic procedure for breast mass classification. Method: The collected image database was composed of 45 malignant and 45 benign breast masses. For each case, tumor segmentation was performed on the B-mode image to obtain tumor contour which was then mapped to the elastographic images to define the corresponding tumor area. The gray-scale pixels around tumor area were classified into white, gray, and black by fuzzy c-means clustering to highlight stiff tissues with darker values. Quantitative strain features were then extracted from the black cluster and compared with the B-mode features in the classification of breast masses. Results: The performance of the proposed strain features achieved an accuracy of 80% (72/90), a sensitivity of 80% (36/45), a specificity of 80% (36/45), and a normalized area under the receiver operating characteristic curve, Az=0.84. Combining the strain features with the B-mode features obtained a significantly better Az=0.93, p-value

Original languageEnglish
Pages (from-to)91-100
Number of pages10
JournalComputers in Biology and Medicine
Publication statusPublished - Sept 1 2015


  • B-mode
  • Breast cancer
  • Computer-aided diagnosis
  • Elastography
  • Fuzzy c-means clustering

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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