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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
[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 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 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 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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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
Figure 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
Figure 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
Figure 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
Figure 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
Figure 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
Figure 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
Figure 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 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 
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[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 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 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 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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