Please wait a minute...
Chin. Phys. B, 2021, Vol. 30(2): 028704    DOI: 10.1088/1674-1056/abcfa7

Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment

Shang-Qu Yan(颜上取)1, Han Zhang(张含)1, Bei Liu(刘备)2, Hao Tang(汤昊)1, and Sheng-You Qian(钱盛友)1,
1 School of Physics and Electronics, Hunan Normal University, Changsha 410081, China; 2 College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
Abstract  In high intensity focused ultrasound (HIFU) treatment, it is crucial to accurately identify denatured and normal biological tissues. In this paper, a novel method based on compressed sensing (CS) and refined composite multi-scale fuzzy entropy (RCMFE) is proposed. First, CS is used to denoise the HIFU echo signals. Then the multi-scale fuzzy entropy (MFE) and RCMFE of the denoised HIFU echo signals are calculated. This study analyzed 90 cases of HIFU echo signals, including 45 cases in normal status and 45 cases in denatured status, and the results show that although both MFE and RCMFE can be used to identify denatured tissues, the intra-class distance of RCMFE on each scale factor is smaller than MFE, and the inter-class distance is larger than MFE. Compared with MFE, RCMFE can calculate the complexity of the signal more accurately and improve the stability, compactness, and separability. When RCMFE is selected as the characteristic parameter, the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE, which helps doctors evaluate the treatment effect more accurately. When the scale factor is selected as 16, the best distinguishing effect can be obtained.
Keywords:  compressed sensing      high intensity focused ultrasound (HIFU) echo signal      multi-scale fuzzy entropy      refined composite multi-scale fuzzy entropy  
Received:  16 September 2020      Revised:  26 October 2020      Accepted manuscript online:  02 December 2020
PACS:  87.85.-d (Biomedical engineering)  
  05.45.-a (Nonlinear dynamics and chaos)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11774088 and 11474090).
Corresponding Authors:  Corresponding author. E-mail:   

Cite this article: 

Shang-Qu Yan(颜上取), Han Zhang(张含), Bei Liu(刘备), Hao Tang(汤昊), and Sheng-You Qian(钱盛友) Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment 2021 Chin. Phys. B 30 028704

1 Maloney E and Hwang J H 2015 Int. J. Hyperthermia 31 302
2 Cranston D 2015 Ultrason. Sonochem. 27 654
3 You Y F, Wang Z G, Ran H T, Zheng Y Y, Wang D, Xu J S, Wang Z B, Chen Y and Li P 2016 Nanoscale 8 4324
4 Bailey M R, Khokhlova V A, Sapozhnikov O A, Kargl S G and Crum L A 2003 Acoust Phys. 49 369
5 Kakiuchi T, Nakayama A, Nojiri J and Matsuo M 2019 Intern. Med. 58 1965
6 Ahmed H U, Bosaily A E S, Brown L C, Gabe R, Kaplan R, Parmar M K, Collaco-Moraes Y, Ward K, Hindley R G, Freeman A, Kirkham A P, Oldroyd R, Parker C and Emberton M 2017 Lancet 389 815
7 Yang C L, Zhu H, Wu S C, Bai Y P and Gao H J 2010 J. Ultrasound Med. 29 1787
8 Liu C C, Dong R, Li B Y, Li Y, Xu F, Ta D and Wang W Q 2019 Chin. Phys. B 28 024302
9 Paris M T, Bell K E, Avrutin E and Mourtzakis M 2020 Clin. Physiol. Funct. Imaging 40 277
10 Seip R, Tavakkoli J, Carlson R F, Wunderlich A, Sanghvi N T, Dines K A and Gardner T A 2002 IEEE Ultrasonics Symposium, October 8-11, 2002, Munich, Germany, p. 1427
11 Donoho D L 2006 IEEE Trans. Inf. Theory 52 1289
12 Chen Y, Liu Z Q and Liu H L 2018 IEEE Access 6 28318
13 Ramdas V, Gorthi S S R K and Mishra D 2016 Int. J. Speech Technol. 19 509
14 Zhang P F, Deng M, Jing J N and Chen K 2020 J. Appl. Geophys 177 104011
15 Arildsen T and Larsen T 2014 Signal Process. 98 275
16 Mobasheri S, Behnam H, Rangraz P and Tavakkoli J 2016 J. Med. Signals Sens. 6 91
17 Richman J S and Moorman J R 2000 Am. J. Physiol.-Heart Circul. Physiol. 278 H2039
18 Chen W T, Zhuang J, Yu W X and Wang Z Z 2009 Med. Eng. Phys. 31 61
19 Chen W T, Wang Z Z, Xie H B and Yu W X 2007 IEEE Trans. Neural Syst. Rehabil. Eng. 15 266
20 Liu C Y, Li K, Zhao L N, Liu F, Zheng D C, Liu C C and Liu S T 2013 Comput. Biol. Med. 43 100
21 Costa M, Goldberger A L and Peng C K 2005 Phys. Rev. E 71 021906
22 Costa M D, Goldberger A L 2015 Entropy 17 1197
23 Zheng J D, Cheng J S, Yang Y and Luo S R 2014 Mech. Mach. Theory. 78 187
24 Fadlallah B, Chen B, Keil A and Pr\'íncipe J 2013 Phys. Rev. E 87 022911
25 Wu S D, Wu C W, Lin S G, Wang C C and Lee K Y 2013 Entropy 15 1069
26 Wu S D, Wu C W, Lin S G, Lee K Y and Peng C K 2014 Phys. Lett. A 378 1369
27 Zheng J D, Pan H Y and Cheng J S 2017 Mech. Syst. Signal Proc. 85 746
28 Aldroubi A, Chen X M and Powell A M 2012 Appl. Comput. Harmon. Anal. 33 282
29 Do T T, Gan L, Nguyen N and Tran T D 2012 IEEE Trans. Signal Process. 60 139
30 Needell D and Vershynin R 2009 Found. Comput. Math. 9 317
31 Tropp J A and Gilbert A C 2007 IEEE Trans. Inf. Theory 53 4655
32 Kira K and Rendell L A 1992 Tenth National Conference on Artificial Intelligence, July 12, 1992, Atlanta, United States, p. 129
33 Liu B, Qian S Y and Hu W P 2019 Entropy 21 666
34 Wang S S and Chen Y 2019 Multidimens. Syst. Signal Process. 30 1517
[1] Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain
Fan Liu(刘璠), Xue-Feng Liu(刘雪峰), Ruo-Ming Lan(蓝若明), Xu-Ri Yao(姚旭日), Shen-Cheng Dou(窦申成), Xiao-Qing Wang(王小庆), and Guang-Jie Zhai(翟光杰). Chin. Phys. B, 2021, 30(1): 014208.
[2] An image compressed sensing algorithm based on adaptive nonlinear network
Yuan Guo(郭媛), Wei Chen(陈炜), Shi-Wei Jing(敬世伟). Chin. Phys. B, 2020, 29(5): 054203.
[3] Compressed ghost imaging based on differential speckle patterns
Le Wang(王乐), Shengmei Zhao(赵生妹). Chin. Phys. B, 2020, 29(2): 024204.
[4] Super-resolution filtered ghost imaging with compressed sensing
Shao-Ying Meng(孟少英), Wei-Wei Shi(史伟伟), Jie Ji(季杰), Jun-Jie Tao(陶俊杰), Qian Fu(付强), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2020, 29(12): 128704.
[5] Compressed sensing sparse reconstruction for coherent field imaging
Bei Cao(曹蓓), Xiu-Juan Luo(罗秀娟), Yu Zhang(张羽), Hui Liu(刘 辉), Ming-Lai Chen(陈明徕). Chin. Phys. B, 2016, 25(4): 040701.
No Suggested Reading articles found!