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非线性超声射频信号熵对乳腺结节良恶性的定征

张玫玫 高凡 屠娟 吴意赟 章东

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非线性超声射频信号熵对乳腺结节良恶性的定征

张玫玫, 高凡, 屠娟, 吴意赟, 章东

Classification of benign and malignant breast masses using entropy from nonlinear ultrasound radiofrequency signal

Zhang Mei-Mei, Gao Fan, Tu Juan, Wu Yi-Yun, Zhang Dong
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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.
      通信作者: 章东, dzhang@nju.edu.cn
    • 基金项目: 江苏省重点研发计划(批准号: BE2018703)和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
      Corresponding author: Zhang Dong, dzhang@nju.edu.cn
    • Funds: Project supported by the Jiangsu Provincial Key R&D Program, China (Grant No. BE2018703) and the Strategic Emerging Industries Science and Technology and Major Achievements Transformation Project of Hunan Province, China (Grant No. 2019GK4046)
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    Jia M, Zheng R, Zhang S, Zeng H, Zou X, Chen W 2015 J. Thorac. Dis. 7 1221

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    Masafumi K, Hiroyuki T 2007 Breast Cancer-Tokyo 14 342Google Scholar

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    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 657Google Scholar

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    Ji D J, Qu G R, Hu C H, Liu B D, Jian J B, Guo X K 2017 Chin. Phys. B 26 0607018

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    Kuhl C K 2000 Eur. Radiol. 10 46Google Scholar

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    方晟, 吴文川, 应葵, 郭华 2013 物理学报 62 048702Google Scholar

    Fang S, Wu W C, Ying K, Guo H 2013 Acta Phys. Sin. 62 048702Google Scholar

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    Mendelson B E, Marcela B V, Berg A W 2013 ACR BI-RADS® Atlas-Breast Ultrasound (Reston: American College of Radiology) pp35−100

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    Li J W, Tong Y Y, Zhou J, Shi Z T, Sun P X, Chang C 2020 J. Ultrasound Med. 39 1589Google Scholar

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    Wojcinski S, Stefanidou N, Hillemanns P, Degenhardt F 2013 Bmc Womens Health 13 47Google Scholar

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    Chang Y W, Chen Y R, Ko C C, Lin W Y, Lin K P 2020 Appl. Sci. 10 1830Google Scholar

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    Koundal D, Gupta S, Singh S 2018 Biomed. Signal Proces. Control 40 117Google Scholar

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    类成新, 吴振森 2010 物理学报 59 5692Google Scholar

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    Weng L, Reid J M, Shankar P M, Soetanto K 1991 J. Acoust. Soc. Am. 89 2992Google Scholar

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    Karmeshu, Agrawal R 2006 Ultrasound Med. Biol. 32 371Google Scholar

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    Tsui P H 2015 Entropy 17 6598Google Scholar

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    Tsui P H, Wan Y L 2016 Entropy 18 341Google Scholar

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    Liu C, Xie L, Kong W, Lu X, Zhang D, Wu M, Zhang L, Yang B 2019 Ultrasonics 99 105951Google Scholar

    [21]

    Zhang D, Gong X F 1999 Ultrasound Med. Biol. 25 593Google 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 1819Google Scholar

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    行鸿彦, 金天力 2010 物理学报 59 140Google Scholar

    Xing H Y, Jin T L 2010 Acta Phys. Sin. 59 140Google Scholar

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    Tranquart F, Grenier N, Eder V, Pourcelot L 1999 Ultrasound Med. Biol. 25 889Google Scholar

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    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)

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    Shan J, Alam S K, Garra B, Zhang Y, Ahmed T 2016 Ultrasound Med. Biol. 42 980Google Scholar

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    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 1218Google Scholar

  • 图 1  (a) 超声RF灰度图像和ROI选取示意图; (b) RF信号线的波形图

    Fig. 1.  (a) Illustration of ultrasound RF gray imaging and selection of ROI; (b) waveforms of RF signals.

    图 2  熵及加权熵成像过程示意图

    Fig. 2.  Illustration of entropy and weighted entropy imaging.

    图 3  某恶性结节表现 (a) 病理; (b) RF灰度图; (c) 熵图; (d) 加权熵图

    Fig. 3.  Presentations of a typical malignant mass: (a) Micrograph; (b) RF grayscale image; (c) entropy image; (d) weighted entropy image.

    图 4  某良性结节表现 (a) 病理; (b) RF灰度图; (c) 熵图; (d)加权熵图

    Fig. 4.  Presentations of a typical benign mass: (a) Micrograph; (b) RF grayscale image; (c) entropy image; (d) weighted entropy image

    图 5  不同参数组合的ROC曲线

    Fig. 5.  ROC curves with various input parameter combinations.

    图 6  不同滑动窗口大小重构的熵图 (a) 0.2倍脉冲长度; (b) 0.5倍脉冲长度; (c) 1.0倍脉冲长度; (d) 2.0倍脉冲长度

    Fig. 6.  The reconstructed entropy images with various window sizes: (a) 0.2 times pulse length; (b) 0.5 times pulse length; (c) 1.0 pulse length; (d) 2.0 times pulse length.

    图 7  306例乳腺RF信号的平均熵值随窗口大小的变化

    Fig. 7.  Dependence of averaged entropy on window size for 306 samples.

    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pAUC准确率/%
    图像灰度5.01 ± 0.974.98 ± 0.930.79
    纵横比0.78 ± 0.371.06 ± 0.366.2 × 10–120.7570.6
    不规则度2.82 ± 1.073.45 ± 1.111.4 × 10–70.6463.7
    深度/mm16.72 ± 5.2920.91 ± 5.494.1 × 10–130.7266.7
    大小/mm2114 ± 142171 ± 1724.5 × 10–40.5958.2
    4.64 ± 0.404.87 ± 0.156.7 × 10–120.7572.5
    加权熵1.69 ± 0.131.76 ± 0.051.7 × 10–110.7469.0
    下载: 导出CSV
  • [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 342Google 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 657Google 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 46Google Scholar

    [7]

    方晟, 吴文川, 应葵, 郭华 2013 物理学报 62 048702Google Scholar

    Fang S, Wu W C, Ying K, Guo H 2013 Acta Phys. Sin. 62 048702Google 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 1589Google Scholar

    [10]

    Wojcinski S, Stefanidou N, Hillemanns P, Degenhardt F 2013 Bmc Womens Health 13 47Google Scholar

    [11]

    Chang Y W, Chen Y R, Ko C C, Lin W Y, Lin K P 2020 Appl. Sci. 10 1830Google Scholar

    [12]

    Koundal D, Gupta S, Singh S 2018 Biomed. Signal Proces. Control 40 117Google Scholar

    [13]

    Burckhardt C B 1978 IEEE Trans. Sonics Ultrason. 25 1Google Scholar

    [14]

    类成新, 吴振森 2010 物理学报 59 5692Google Scholar

    Lei C X, Wu Z S 2010 Acta Phys. Sin. 59 5692Google 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 2992Google Scholar

    [17]

    Karmeshu, Agrawal R 2006 Ultrasound Med. Biol. 32 371Google Scholar

    [18]

    Tsui P H 2015 Entropy 17 6598Google Scholar

    [19]

    Tsui P H, Wan Y L 2016 Entropy 18 341Google Scholar

    [20]

    Liu C, Xie L, Kong W, Lu X, Zhang D, Wu M, Zhang L, Yang B 2019 Ultrasonics 99 105951Google Scholar

    [21]

    Zhang D, Gong X F 1999 Ultrasound Med. Biol. 25 593Google 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 1819Google 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 140Google Scholar

    Xing H Y, Jin T L 2010 Acta Phys. Sin. 59 140Google Scholar

    [26]

    Shannon C E 1948 Bell Syst. Tech. J. 27 379Google Scholar

    [27]

    Guiasu S 1986 J. Stat. Plan. Infer. 15 63Google Scholar

    [28]

    Tranquart F, Grenier N, Eder V, Pourcelot L 1999 Ultrasound Med. Biol. 25 889Google Scholar

    [29]

    Ward B, Baker A C, Humphrey V F 1997 J. Acoust. Soc. Am. 101 143Google Scholar

    [30]

    Rosen E L, Soo M S 2001 Clin. Imag. 25 379Google 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 980Google 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 1218Google Scholar

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出版历程
  • 收稿日期:  2020-11-15
  • 修回日期:  2020-12-13
  • 上网日期:  2021-04-08
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