Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging. Issue 3 (December 2015)

• Record Type: Journal Article
• Authors: Lo, Chung-Ming; Lai, Yi-Chen; Chou, Yi-Hong; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 122:Issue 3(2015)
• Page Start: 354
• Page End: 361
• Publication Date: 2015-12
• Subjects: Breast cancer -- Shear-wave elastography -- Computer-aided diagnosis -- Histogram moment -- Vector quantification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.09.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A computer-aided diagnosis (CAD) system based on the quantified color distributions in shear wave elastography (SWE) was developed. The combination of the proposed CAD system using SWE features and BI-RADS assessment would provide a promising diagnostic suggestion. Spatial correlation was combined with the kPa-weighted color information as a feature to reduce the influence of noises far from tumor. Abstract: 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 <0.05). Conclusions: The combination of the proposed CAD system based on SWE features and the BI-RADS assessment would provide a promising diagnostic suggestion.