Department of Information Management, Chang Gung University, Tao-Yuan, Taiwan.
Comput Med Imaging Graph. 2012 Dec;36(8):627-33. doi: 10.1016/j.compmedimag.2012.07.004. Epub 2012 Aug 30.
To promote the classification accuracy and decrease the time of extracting features and finding (near) optimal classification model of an ultrasound breast tumor image computer-aided diagnosis system, we propose an approach which simultaneously combines feature selection and parameter setting in this study. In our approach ultrasound breast tumors were segmented automatically by a level set method. The auto-covariance texture features and morphologic features were first extracted following the use of a genetic algorithm to detect significant features and determine the near-optimal parameters for the support vector machine (SVM) to identify the tumor as benign or malignant. The proposed CAD system can differentiate benign from malignant breast tumors with high accuracy and short feature extraction time. According to the experimental results, the accuracy of the proposed CAD system for classifying breast tumors is 95.24% and the computing time of the proposed system for calculating features of all breast tumor images is only 8% of that of a system without feature selection. Furthermore, the time of finding (near) optimal classification model is significantly than that of grid search. It is therefore clinically useful in reducing the number of biopsies of benign lesions and offers a second reading to assist inexperienced physicians in avoiding misdiagnosis.
为了提高超声乳腺肿瘤图像计算机辅助诊断系统的分类准确率，减少特征提取和寻找（近）最优分类模型的时间，我们提出了一种在本研究中同时结合特征选择和参数设置的方法。在我们的方法中，使用水平集方法自动分割超声乳腺肿瘤。首先提取自协方差纹理特征和形态特征，然后使用遗传算法检测显著特征，并确定支持向量机（SVM）的近最优参数，以识别肿瘤是良性还是恶性。所提出的 CAD 系统可以以较高的准确率和较短的特征提取时间将良性和恶性乳腺肿瘤区分开来。根据实验结果，所提出的 CAD 系统对乳腺肿瘤的分类准确率为 95.24%，而所提出的系统计算所有乳腺肿瘤图像特征的计算时间仅为没有特征选择的系统的 8%。此外，寻找（近）最优分类模型的时间明显短于网格搜索。因此，它在临床上有助于减少良性病变的活检数量，并提供二次阅读，以帮助经验不足的医生避免误诊。
