Shankar P Mohana, Dumane Vishruta A, Piccoli Catherine W, Reid John M, Forsberg Flemming, Goldberg Barry B
Department of Electrical and Computing Engineering, Drexel University, Philadelphia, PA 19104, USA.
IEEE Trans Ultrason Ferroelectr Freq Control. 2003 Aug;50(8):1002-9. doi: 10.1109/tuffc.2003.1226544.
Classification of breast masses in ultrasonic B-scan images is undertaken using a multiparameter approach. The parameters are generated on the basis of a non-Rayleigh statistic model of the backscattered envelope from the breast tissue. They can be computed automatically with minimal clinical intervention once the location of the mass is known. A new discriminant is developed that combines these parameters linearly. It is seen that this new discriminant performs classification of masses into benign or malignant better than the classification by any one of the individual parameters. The data set studied consisted of 99 cases (70 patients with benign masses and 29 patients with malignant masses). The areas under the receiver operating characteristic (ROC) curves (Az) and statistical attributes of the areas were studied to establish the enhancement in performance. The Az value after combining all the parameters was found to be 0.8701. Upon combining this parameter with the level of suspicion (LOS) scores of a radiologist, the performance is further enhanced with an area under the (empirical) ROC of 0.94 having an operating point at a sensitivity of 0.965 and specificity of 0.87. It is suggested that this automated approach may hold promise as a means of classifying breast masses.
利用多参数方法对超声B扫描图像中的乳腺肿块进行分类。这些参数是基于乳腺组织后向散射包络的非瑞利统计模型生成的。一旦知道肿块的位置，只需极少的临床干预就可以自动计算出这些参数。开发了一种新的判别方法，将这些参数进行线性组合。结果表明，这种新的判别方法在将肿块分为良性或恶性方面比任何单个参数的分类方法表现更好。所研究的数据集包括99个病例（70例良性肿块患者和29例恶性肿块患者）。研究了接收器操作特征（ROC）曲线下的面积（Az）以及这些面积的统计属性，以确定性能的提升。发现将所有参数组合后的Az值为0.8701。将该参数与放射科医生的可疑程度（LOS）评分相结合后，（经验）ROC曲线下的面积进一步增大，达到0.94，操作点的灵敏度为0.965，特异性为0.87。有人提出，这种自动化方法有望成为一种乳腺肿块分类手段。
