Proposal of Semi-automatic Classification of Breast Lesions for Strain Sonoelastography Using a Dedicated CAD System
The aim of this study was to develop a tool to classify breast lesions using ultrasound elastography. Our dataset included a total of 78 patients enrolled for percutaneous biopsy of 85 breast lesions. These lesions were classified into three sonoelastographic scores, where scores of 1 and 2 were considered negative – soft and intermediate respectively; the score 3 was considered positive – hard. The visual classification of elastography performed by two radiologists was compared with our semi-automatic method. This classification aims to segment the red pixels found in the color elastography, quantify them and characterize the lesion by comparing the areas in red with the manually segmented lesion by the two radiologists. Our semi-automated technique had comparable performance to that of the two radiologists: sensitivity of 54.5 % and specificity of 90.5 %. The agreement kappa was greater than 0.8 for all observers. Thus, we concluded that the proposed method achieved a high rate of agreement between observers. In addition, the method presented high diagnostic specificity in classifying breast elastography images. By including more image features in the future, we expect our classifier can be use to standardize the classification of breast elastography.
KeywordsBreast cancer Elastography Classification Color map
To FAPESP (2015/17302-5) for the financial support.
- 1.Kumm, T.R., Szabunio, M.M.: Elastography for the characterization of breast lesions: initial clinical experience. Control Cancer 17, 156–161 (2010)Google Scholar
- 2.Au, F.W., Ghail, S., Moshonov, H., Kahn, H., Brennan, C., Dua, H., Crystal, P.: Diagnostic performance of quantitative shear wave elastography in the evaluation of solid breast masses: determination of the most discriminatory parameter. AJR Am. J. Roentgenol. 203, W328–W336 (2014)CrossRefGoogle Scholar
- 5.Fleury, E.F.C., Fleury, J.C.V., Piato, S., Roveda Jr., D.: New elastographic classification of breast lesions during and after compression. Diagn. Interv. Radiol. 15, 96–103 (2009)Google Scholar
- 6.Ganesan, P., Rajini, V.: Segmentation and edge detection of color images using CIELAB color space and edge detectors. Emerg. Trends Robot. Commun. Technol. (INTERACT), 393–397 (2010)Google Scholar
- 7.Baldevbhai, P.J., Anand, R.S.: Color image segmentation for medical images using L*a*b* color space. Electron. Commun. Eng. 1(2), 24–45 (2012)Google Scholar
- 8.Pei, C., Wang, C., Xu, S.: Segmentation of the breast region in mammograms using marker-controlled watershed transform. In: 2nd International Conference on Information Science and Engineering, pp. 2371–2374 (2010)Google Scholar