IEEE Trans Med Imaging. 2013 Dec;32(12):2262-73. doi: 10.1109/TMI.2013.2279938. Epub 2013 Aug 29.
Computer-aided diagnosis (CAD) systems in gray-scale breast ultrasound images have the potential to reduce unnecessary biopsy of breast masses. The purpose of our study is to develop a robust CAD system based on the texture analysis. First, gray-scale invariant features are extracted from ultrasound images via multi-resolution ranklet transform. Thus, one can apply linear support vector machines (SVMs) on the resulting gray-level co-occurrence matrix (GLCM)-based texture features for discriminating the benign and malignant masses. To verify the effectiveness and robustness of the proposed texture analysis, breast ultrasound images obtained from three different platforms are evaluated based on cross-platform training/testing and leave-one-out cross-validation (LOO-CV) schemes. We compare our proposed features with those extracted by wavelet transform in terms of receiver operating characteristic (ROC) analysis. The AUC values derived from the area under the curve for the three databases via ranklet transform are 0.918 (95% confidence interval [CI], 0.848 to 0.961), 0.943 (95% CI, 0.906 to 0.968), and 0.934 (95% CI, 0.883 to 0.961), respectively, while those via wavelet transform are 0.847 (95% CI, 0.762 to 0.910), 0.922 (95% CI, 0.878 to 0.958), and 0.867 (95% CI, 0.798 to 0.914), respectively. Experiments with cross-platform training/testing scheme between each database reveal that the diagnostic performance of our texture analysis using ranklet transform is less sensitive to the sonographic ultrasound platforms. Also, we adopt several co-occurrence statistics in terms of quantization levels and orientations (i.e., descriptor settings) for computing the co-occurrence matrices with 0.632+ bootstrap estimators to verify the use of the proposed texture analysis. These experiments suggest that the texture analysis using multi-resolution gray-scale invariant features via ranklet transform is useful for designing a robust CAD system.
计算机辅助诊断（CAD）系统在灰阶乳腺超声图像中具有减少乳腺肿块不必要活检的潜力。我们的研究目的是开发一个基于纹理分析的强大 CAD 系统。首先，通过多分辨率秩列变换从超声图像中提取灰度不变特征。因此，可以在线性支持向量机（SVM）上应用基于灰度共生矩阵（GLCM）的纹理特征来区分良性和恶性肿块。为了验证所提出的纹理分析的有效性和鲁棒性，基于跨平台训练/测试和留一交叉验证（LOO-CV）方案，评估了来自三个不同平台的乳腺超声图像。我们在 ROC 分析方面比较了基于小波变换提取的特征和我们提出的特征。通过秩列变换从三个数据库获得的曲线下面积的 AUC 值分别为 0.918（95%置信区间[CI]，0.848 至 0.961）、0.943（95%CI，0.906 至 0.968）和 0.934（95%CI，0.883 至 0.961），而基于小波变换的 AUC 值分别为 0.847（95%CI，0.762 至 0.910）、0.922（95%CI，0.878 至 0.958）和 0.867（95%CI，0.798 至 0.914）。在每个数据库之间进行跨平台训练/测试方案的实验表明，使用秩列变换的纹理分析的诊断性能对超声超声平台的敏感性较低。此外，我们采用了几种共现统计量，根据量化水平和方向（即描述符设置）来计算共现矩阵，并使用 0.632+自举估计器来验证所提出的纹理分析的使用。这些实验表明，使用多分辨率灰度不变特征通过秩列变换进行纹理分析对于设计强大的 CAD 系统是有用的。
