| 非线性超声射频信号熵对乳腺结节良恶性的定征 |
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| 引用本文： | 张玫玫,高凡,屠娟,吴意赟,章东. 非线性超声射频信号熵对乳腺结节良恶性的定征[J]. 物理学报, 2021, 0(8): 177-185 |
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| 作者姓名： | 张玫玫 高凡 屠娟 吴意赟 章东 |
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| 作者单位： | 南京大学物理学院;江苏省中医院超声科 |
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| 基金项目： | 江苏省重点研发计划(批准号:BE2018703);湖南省战略性新兴产业科技攻关与重大成果转化项目(批准号:2019GK4046)资助的课题。 |
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| 摘 要： | 本文提出了一种基于非线性超声射频(radio frequency, RF)信号熵对乳腺结节良恶性进行定征的方法.对306例乳腺结节样本(良性158例,恶性148例)提取了基于超声RF信号二次谐波的熵和加权熵,以及常规超声参数(图像灰度、纵横比、不规则度、乳腺结节大小、深度);采用t检验和线性分类器检测参数对乳腺结节良恶性的区分度;进一步将有效参数组合输入支持向量机对乳腺结节良恶性进行分类.结果表明:除图像灰度外,其余参数均在乳腺结节的良性与恶性间有显著差异.多参数结合输入支持向量机的良恶性分类的准确率、敏感性和特异性分别为81.4%, 78.4%和84.2%.本文工作表明非线性超声RF信号的熵可有效地定征乳腺结节的良恶性,有望成为乳腺结节良恶性定征新参量.
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| 关 键 词： | 熵 加权熵 乳腺超声 组织定征 非线性 |
Classification of benign and malignant breast masses using entropy from nonlinear ultrasound radiofrequency signal |
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| Zhang Mei-Mei,Gao Fan,Tu Juan,Wu Yi-Yun,Zhang Dong. Classification of benign and malignant breast masses using entropy from nonlinear ultrasound radiofrequency signal[J]. Acta Physica Sinica, 2021, 0(8): 177-185 |
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| Authors: | Zhang Mei-Mei Gao Fan Tu Juan Wu Yi-Yun Zhang Dong |
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| Affiliation: | (Key Laboratory of Modern Acoustics of the Ministry of Education,School of Physics,Nanjing University,Nanjing 210093,China;Department of Ultrasound,Jiangsu Provincial Hospital of Chinese Medicine,Nanjing 210029,China) |
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| Abstract: | In this paper the classification of benign and malignant breast masses is investigated by using the entropy of nonlinear ultrasound radio frequency(RF)signal.The parameters(entropy and weighted entropy)derived from the nonlinear ultrasound RF signal and the conventional ultrasound parameters(image grayscale,aspect ratio,irregularity,breast mass size,and depth)are extracted from 306 image samples(158 benign and 148 malignant);t-test and linear-discriminant classifier(LDC)are used to test the distinction between benign and malignant breast masses by each parameter;furthermore the effective parameters are combined to classify benign and malignant breast masses.The results show that except the image grayscale,the other parameters are significantly different between benign and malignant breast masses.Multi-parameter combined with support vector machine(SVM)is used to classify breast masses as benign and malignant.The accuracy is 81.4%,the sensitivity is 78.4%,and the specificity is 84.2%.The present work shows that the combination of the nonlinear entropy of ultrasound RF signal and traditional ultrasound parameters can more effectively characterize the benign and malignant breast masses.The entropy of nonlinear ultrasound RF signal can become a new parameter for characterizing the benign and malignant breast masses. |
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| Keywords: | entropy weighted entropy breast ultrasound tissue characterization nonlinear |
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