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

• Quantitative strain features wereextracted fromelastographic images to express tissue elasticity.
• A computer-aided diagnosis system based on the quantitative strain features was developed to classify breast masses.
• Combining the strain features with the B-mode features obtained a significantly better performance in malignancy evaluation.

BackgroundElastography 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׳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.MethodThe 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.ResultsThe 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<0.05.ConclusionsSummarily, the quantified strain features can be combined with the B-mode features to provide a promising suggestion in distinguishing malignant from benign tumors.