Ardakani Ali Abbasian, Gharbali Akbar, Mohammadi Afshin
Student Research Committee (A.A.A.) and Department of Medical Physics, Faculty of Medicine (A.G.), Urmia University of Medical Sciences, Urmia, Iran; and Department of Radiology, Faculty of Medicine, Imam Khomeini Hospital, Urmia University of Medical Sciences, Urmia, Iran (A.M.).
J Ultrasound Med. 2015 Feb;34(2):225-31. doi: 10.7863/ultra.34.2.225.
The purpose of this study was to evaluate a computer-aided diagnostic system with texture analysis to improve radiologists' accuracy in identification of breast tumors as malignant or benign.
The database included 20 benign and 12 malignant tumors. We extracted 300 statistical texture features as descriptors for each selected region of interest in 3 normalization schemes (default, μ - 3σ, and μ + 3σ, where μ and σ were the mean value and standard deviation, respectively, of the gray-level intensity and 1%-99%). Then features determined by the Fisher coefficient and the lowest probability of classification error + average correlation coefficient yielded the 10 best and most effective features. We analyzed these features under 2 standardization states (standard and nonstandard). For texture analysis of the breast tumors, we applied principle component, linear discriminant, and nonlinear discriminant analyses. First-nearest neighbor classification was performed for the features resulting from the principle component and linear discriminant analyses. Nonlinear discriminant analysis features were classified by an artificial neural network. Receiver operating characteristic curve analysis was used for examining the performance of the texture analysis methods.
Standard feature parameters extracted by the Fisher coefficient under the default and 3σ normalization schemes via nonlinear discriminant analysis showed high performance for discrimination between benign and malignant tumors, with sensitivity of 94.28%, specificity of 100%, accuracy of 97.80%, and an area under the receiver operating characteristic curve of 0.9714.
Texture analysis is a reliable method and has the potential to be used effectively for classification of benign and malignant tumors on breast sonography.
本研究旨在评估一种具有纹理分析功能的计算机辅助诊断系统，以提高放射科医生鉴别乳腺肿瘤良恶性的准确性。
数据库包含20个良性肿瘤和12个恶性肿瘤。我们在3种归一化方案（默认、μ - 3σ和μ + 3σ，其中μ和σ分别为灰度强度和1%-99%的均值和标准差）下，为每个选定的感兴趣区域提取300个统计纹理特征作为描述符。然后，由Fisher系数以及最低分类错误概率+平均相关系数确定的特征产生了10个最佳且最有效的特征。我们在2种标准化状态（标准和非标准）下分析这些特征。对于乳腺肿瘤的纹理分析，我们应用了主成分分析、线性判别分析和非线性判别分析。对主成分分析和线性判别分析得到的特征进行最近邻分类。非线性判别分析特征由人工神经网络进行分类。采用受试者工作特征曲线分析来检验纹理分析方法的性能。
通过非线性判别分析，在默认和3σ归一化方案下由Fisher系数提取的标准特征参数在鉴别良性和恶性肿瘤方面表现出高性能，灵敏度为94.28%，特异性为100%，准确率为97.80%，受试者工作特征曲线下面积为0.9714。
纹理分析是一种可靠的方法，有潜力有效地用于乳腺超声检查中良性和恶性肿瘤的分类。
