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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.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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表 1 特征参数的分布
Table 1. Distribution of various parameters.
参数 平均值 ± 标准差 双样本t检验(p < 0.05) 线性分类器(LDC) 良性 恶性 差异性 p值 AUC 准确率/% 图像灰度 5.01 ± 0.97 4.98 ± 0.93 否 0.79 纵横比 0.78 ± 0.37 1.06 ± 0.36 是 6.2 × 10–12 0.75 70.6 不规则度 2.82 ± 1.07 3.45 ± 1.11 是 1.4 × 10–7 0.64 63.7 深度/mm 16.72 ± 5.29 20.91 ± 5.49 是 4.1 × 10–13 0.72 66.7 大小/mm2 114 ± 142 171 ± 172 是 4.5 × 10–4 0.59 58.2 熵 4.64 ± 0.40 4.87 ± 0.15 是 6.7 × 10–12 0.75 72.5 加权熵 1.69 ± 0.13 1.76 ± 0.05 是 1.7 × 10–11 0.74 69.0 -
[1] Jia M, Zheng R, Zhang S, Zeng H, Zou X, Chen W 2015 J. Thorac. Dis. 7 1221
[2] Masafumi K, Hiroyuki T 2007 Breast Cancer-Tokyo 14 342
Google Scholar
[3] Tahoces G P, Correa J, Souto M, Gonzalez C, Gomez L 1991 IEEE Trans. Med. Imaging 30 330
[4] Boone J M, Nelson T R, Lindfors K K, Seibert J A 2001 Radiology 221 657
Google Scholar
[5] Ji D J, Qu G R, Hu C H, Liu B D, Jian J B, Guo X K 2017 Chin. Phys. B 26 0607018
[6] Kuhl C K 2000 Eur. Radiol. 10 46
Google Scholar
[7] 方晟, 吴文川, 应葵, 郭华 2013 物理学报 62 048702
Google Scholar
Fang S, Wu W C, Ying K, Guo H 2013 Acta Phys. Sin. 62 048702
Google Scholar
[8] Mendelson B E, Marcela B V, Berg A W 2013 ACR BI-RADS® Atlas-Breast Ultrasound (Reston: American College of Radiology) pp35−100
[9] Li J W, Tong Y Y, Zhou J, Shi Z T, Sun P X, Chang C 2020 J. Ultrasound Med. 39 1589
Google Scholar
[10] Wojcinski S, Stefanidou N, Hillemanns P, Degenhardt F 2013 Bmc Womens Health 13 47
Google Scholar
[11] Chang Y W, Chen Y R, Ko C C, Lin W Y, Lin K P 2020 Appl. Sci. 10 1830
Google Scholar
[12] Koundal D, Gupta S, Singh S 2018 Biomed. Signal Proces. Control 40 117
Google Scholar
[13] Burckhardt C B 1978 IEEE Trans. Sonics Ultrason. 25 1
Google Scholar
[14] 类成新, 吴振森 2010 物理学报 59 5692
Google Scholar
Lei C X, Wu Z S 2010 Acta Phys. Sin. 59 5692
Google Scholar
[15] Wagner R F, Insana M F, Brown D G 1987 J. Opt. Soc. Am. A: 4 910
[16] Weng L, Reid J M, Shankar P M, Soetanto K 1991 J. Acoust. Soc. Am. 89 2992
Google Scholar
[17] Karmeshu, Agrawal R 2006 Ultrasound Med. Biol. 32 371
Google Scholar
[18] Tsui P H 2015 Entropy 17 6598
Google Scholar
[19] Tsui P H, Wan Y L 2016 Entropy 18 341
Google Scholar
[20] Liu C, Xie L, Kong W, Lu X, Zhang D, Wu M, Zhang L, Yang B 2019 Ultrasonics 99 105951
Google Scholar
[21] Zhang D, Gong X F 1999 Ultrasound Med. Biol. 25 593
Google Scholar
[22] Gong X F, Zhang D, Liu J H, Wang H L, Yan Y S, Xu X C 2004 J. Acoust. Soc. Am. 116 1819
Google Scholar
[23] Cortes C, Vapnik V 1995 Machine Learning 20 273
[24] Chang C C, Lin C J 2011 Acm T. Intel. Syst. Tec. 2 1
[25] 行鸿彦, 金天力 2010 物理学报 59 140
Google Scholar
Xing H Y, Jin T L 2010 Acta Phys. Sin. 59 140
Google Scholar
[26] Shannon C E 1948 Bell Syst. Tech. J. 27 379
Google Scholar
[27] Guiasu S 1986 J. Stat. Plan. Infer. 15 63
Google Scholar
[28] Tranquart F, Grenier N, Eder V, Pourcelot L 1999 Ultrasound Med. Biol. 25 889
Google Scholar
[29] Ward B, Baker A C, Humphrey V F 1997 J. Acoust. Soc. Am. 101 143
Google Scholar
[30] Rosen E L, Soo M S 2001 Clin. Imag. 25 379
Google Scholar
[31] 周志华 2016 机器学习 (北京: 清华大学出版社) pp121−140
Zhou Z H 2016 Machine Learning (Beijing: Tsinghua University Press) pp121−140 (in Chinese)
[32] Box J F 1987 Stat. Sci. 2 45
[33] Shan J, Alam S K, Garra B, Zhang Y, Ahmed T 2016 Ultrasound Med. Biol. 42 980
Google Scholar
[34] Yap M H, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison A K, Marti R, Moi Hoon Y, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison A K, Marti R 2018 IEEE J. Biomed. Health Inform. 22 1218
Google Scholar
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