Design and implementation for portable ultrasound-aided breast cancer screening system_Journal of Biomedical Engineering

Authors：

WANG Zhicheng ¹ ,  HE Bingbing ¹ , ZHANG Yufeng ¹ , LI Zhiyao ² , YAO Ruihan ¹ , HUANG Kai ¹

1. The Department of Electronic Engineering, School of Information, Yunnan University, Kunming 650091, P. R. China;
2. The Department of Ultrasound, the Third Affiliated Hospital of Kunming Medical College, Kunming 650118, P. R. China;

Corresponding author：

HE Bingbing, Email: hebingbing@ynu.edu.cn

Keywords：

Clarius portable ultrasound; Ultrasound radio frequency data; Breast cancer screening; Computer-aided system

DOI：

10.7507/1001-5515.202108015

Video：

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Abstract Full text Figures/Tables Video References Cited by

Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.

Citation： WANG Zhicheng, HE Bingbing, ZHANG Yufeng, LI Zhiyao, YAO Ruihan, HUANG Kai. Design and implementation for portable ultrasound-aided breast cancer screening system. Journal of Biomedical Engineering, 2022, 39(2): 390-397. doi: 10.7507/1001-5515.202108015 Copy

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1. 中华人民共和国国家卫生健康委员会. 2019年我国卫生健康事业发展统计公报. 中国实用乡村医生杂志, 2020, 27(9): 1-11.
2. 中国抗癌协会乳腺癌专业委员会. 中国抗癌协会乳腺癌诊治指南与规范(2019年版). 中国癌症杂志, 2019, 29(8): 609-679.
3. Chang Y W, Chen Y R, Ko C C, et al. A novel computer-aided-diagnosis system for breast ultrasound images based on BI-RADS categories. Applied Sciences, 2020, 10(5): 1-20.
4. Mathworks. Statistics and machine learning toolbox™ user's guide. Natick: Mathworks Ins, 2021: 1806-2046.
5. Chen P H, Fan R E, Lin C J. A study on SMO-type decomposition methods for support vector machines. IEEE Transactions on Neural Network, 2006, 17(4): 893-908.
6. Koun D D, Gupta S, Singh S. Computer aided thyroid nodule detection system using medical ultrasound images. Biomedical Signal Processing & Control, 2018, 40(3): 117-130.
7. 邹波. 基于超声射频时间序列的乳腺病灶良恶性分类. 广州: 华南理工大学, 2018.
8. 林春漪, 邹波, 周建华. 基于超声射频时间序列分析的乳腺病灶良恶性分类. 生物医学工程研究, 2018, 37(1): 21-26.
9. 何培乾, 程阳阳, 赵彦群. 便携式超声设备: 中国, CN213525217U. 2021-05-28.
10. 严郁, 朱伟, 蔡润秋, 等. 基于超声射频信号的乳腺肿瘤分级算法研究. 南京理工大学学报, 2018, 42(4): 385-391.
11. 陆文周. Qt 5开发及实例. 北京: 电子工业出版社, 2014: 22-401.
12. 罗洪艳, 徐旭, 高成龙, 等. 基于QT的磁共振质量检测自动分析系统的设计与实现. 生物医学工程学杂志, 2019, 36(4): 627-632.
13. 陈彬. 基于多阈值Otsu关键算法的乳腺钼靶图像肿块分割研究. 兰州: 兰州交通大学, 2018.
14. Tsui P H, Chen C K, Kuo W H, et al. Small-window parametric imaging based on information entropy for ultrasound tissue characterization. Scientific Reports, 2017, 7(1): 41004.
15. 高东平, 刘慧, 池慧. 乳腺肿瘤超声图像特征参数量化研究进展. 北京生物医学工程, 2011, 30(6): 656-660.
16. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 1979, 9(1): 62-66.
17. 王燕, 李积英, 杨宜林, 等. 基于SLIC和GVF Snake算法的乳腺肿瘤分割. 激光与光电子学进展, 2020, 57(14): 141023.
18. 张玫玫, 高凡, 屠娟, 等. 非线性超声射频信号熵对乳腺结节良恶性的定征. 物理学报, 2021, 70(8): 084302.
19. Piotrzkowska-Wróblewska H, Dobruch-Sobczak K, Byra M, et al. Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions. Med Phys, 2017, 44(11): 6105-6109.
20. Steifer T, Lewandowski M. Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification. Biomed Signal Process Control, 2019, 51(1): 235-242.
21. Byra M. Discriminant analysis of neural style representations for breast lesion classification in ultrasound. Biocybern Biomed Eng, 2018, 38(3): 684-690.
22. Ouyang Y, Tsui P H, Wu S, et al. Classification of benign and malignant breast tumors using h-scan ultrasound imaging. Diagnostics (Basel), 2019, 9(4): 182.
23. Jarosik P, Klimonda Z, Lewandowski M, et al. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks. Biocybern Biomed Eng, 2020, 40(3): 977-986.

Journal of Biomedical Engineering

Design and implementation for portable ultrasound-aided breast cancer screening system

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