RATIONALE AND OBJECTIVES: Breast sonography is not routinely used to distinguish benign from malignant solid masses because of considerable overlap in their sonographic appearances. The purpose of this study was to investigate the computerized analyses of breast lesions in ultrasonographic (US) images in order to ultimately aid in the task of discriminating between malignant and benign lesions. MATERIALS AND METHODS: Features related to lesion margin, shape, homogeneity (texture), and posterior acoustic attenuation pattern in US images of the breast were extracted and calculated. The study database contained 184 digitized US images from 58 patients with 78 lesions. Benign lesions were confirmed at biopsy or cyst aspiration or with image interpretation alone; malignant lesions were confirmed at biopsy. Performance of the various individual features and output from linear discriminant analysis in distinguishing benign from malignant lesions was studied by using receiver operating characteristic (ROC) analysis. RESULTS: At ROC analysis, the feature characterizing the margin yielded Az values (area under the ROC curve) of 0.85 and 0.75 in distinguishing between benign and malignant lesions for the entire database and for an "equivocal" database, respectively. The equivocal database contained lesions that had been proved to be benign or malignant at cyst aspiration or biopsy. Linear discriminant analysis round-robin runs yielded Az values of 0.94 and 0.87 in distinguishing benign from malignant lesions for the entire database and for the equivocal database, respectively. CONCLUSION: Computerized analysis of US images has the potential to increase the specificity of breast sonography.
RATIONALE AND OBJECTIVES: Breast sonography is not routinely used to distinguish benign from malignant solid masses because of considerable overlap in their sonographic appearances. The purpose of this study was to investigate the computerized analyses of breast lesions in ultrasonographic (US) images in order to ultimately aid in the task of discriminating between malignant and benign lesions. MATERIALS AND METHODS: Features related to lesion margin, shape, homogeneity (texture), and posterior acoustic attenuation pattern in US images of the breast were extracted and calculated. The study database contained 184 digitized US images from 58 patients with 78 lesions. Benign lesions were confirmed at biopsy or cyst aspiration or with image interpretation alone; malignant lesions were confirmed at biopsy. Performance of the various individual features and output from linear discriminant analysis in distinguishing benign from malignant lesions was studied by using receiver operating characteristic (ROC) analysis. RESULTS: At ROC analysis, the feature characterizing the margin yielded Az values (area under the ROC curve) of 0.85 and 0.75 in distinguishing between benign and malignant lesions for the entire database and for an "equivocal" database, respectively. The equivocal database contained lesions that had been proved to be benign or malignant at cyst aspiration or biopsy. Linear discriminant analysis round-robin runs yielded Az values of 0.94 and 0.87 in distinguishing benign from malignant lesions for the entire database and for the equivocal database, respectively. CONCLUSION: Computerized analysis of US images has the potential to increase the specificity of breast sonography.