Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging

Chung Ming Lo, Yi Chen Lai, Yi Hong Chou, Ruey Feng Chang

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Background and objectives: A computer-aided diagnosis (CAD) system based on the quantified color distributions in shear-wave elastography (SWE) was developed to evaluate the malignancies of breast tumors. Methods: For 57 benign and 31 malignant tumors, 18 SWE features were extracted from regions of interest (ROI), including the tumor and peritumoral areas. In the ROI, a histogram in each color channel was described using moments such as the mean, variance, skewness, and kurtosis. Moreover, three color channels were combined as a vector to evaluate tissue elasticity. The SWE features were then combined in a logistic regression classifier for breast tumor classification. Results: The performance of the CAD system achieved an accuracy of 81%. Combining the CAD system with a BI-RADS assessment obtained an Az improvement from 0.77 to 0.89 (p-value

Original languageEnglish
Pages (from-to)354-361
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Issue number3
Publication statusPublished - Dec 2015


  • Breast cancer
  • Computer-aided diagnosis
  • Histogram moment
  • Shear-wave elastography
  • Vector quantification

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


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