PURPOSE: To develop an ultrasound computer-aided diagnosis (CAD) system using speckle features of automated breast ultrasound (ABUS) images. METHODS: The ABUS images of 147 pathologically proven breast masses (76 benign and 71 malignant cases) were used. For each mass, a volume of interest (VOI) was cropped to define the tumor area, and the average number of speckle pixels within a VOI was calculated. In addition, first-order and second-order statistical analyses of the speckle pixels were used to quantify the information of gray-level distributions and the spatial relations among the pixels. Receiver operating characteristic curve analysis was used to evaluate the performance. RESULTS: The proposed CAD system based on speckle patterns achieved an accuracy of 84.4% (124∕147), a sensitivity of 83.1% (59∕71), a specificity of 85.5% (65∕76), and an Az of 0.91. The performance indices of the speckle features were comparable to the performance indices of the morphological features, which include shape and ellipse-fitting features (p-value > 0.05). Furthermore, combining speckle and morphological features yielded an Az that was significantly better than the Az of the morphological features alone (0.96 vs 0.91, p-value = 0.0154). CONCLUSIONS: The results suggest that the proposed speckle features, while combined with morphological features, are promising for the classification of breast masses detected using ABUS.
PURPOSE: To develop an ultrasound computer-aided diagnosis (CAD) system using speckle features of automated breast ultrasound (ABUS) images. METHODS: The ABUS images of 147 pathologically proven breast masses (76 benign and 71 malignant cases) were used. For each mass, a volume of interest (VOI) was cropped to define the tumor area, and the average number of speckle pixels within a VOI was calculated. In addition, first-order and second-order statistical analyses of the speckle pixels were used to quantify the information of gray-level distributions and the spatial relations among the pixels. Receiver operating characteristic curve analysis was used to evaluate the performance. RESULTS: The proposed CAD system based on speckle patterns achieved an accuracy of 84.4% (124∕147), a sensitivity of 83.1% (59∕71), a specificity of 85.5% (65∕76), and an Az of 0.91. The performance indices of the speckle features were comparable to the performance indices of the morphological features, which include shape and ellipse-fitting features (p-value > 0.05). Furthermore, combining speckle and morphological features yielded an Az that was significantly better than the Az of the morphological features alone (0.96 vs 0.91, p-value = 0.0154). CONCLUSIONS: The results suggest that the proposed speckle features, while combined with morphological features, are promising for the classification of breast masses detected using ABUS.
Authors: Laith R Sultan; Ghizlane Bouzghar; Benjamin J Levenback; Nauroze A Faizi; Santosh S Venkatesh; Emily F Conant; Chandra M Sehgal Journal: Adv Breast Cancer Res Date: 2015-01-09