Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation_Journal of Biomedical Engineering

Authors：

LI Yan ^1,2 , XIA Zeyang ¹ , WU Xiaojun ² ,  XIONG Jing ¹

1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China;
2. Mechanical and Electrical Engineering College, Xi'an University of Architecture and Technology, Xi’an 710055, P. R. China;

Corresponding author：

XIONG Jing, Email: jing.xiong@siat.ac.cn

Keywords：

Ultrasound image; Quality assessment; Soft tissue deformation; Correlation coefficient

DOI：

10.7507/1001-5515.202011077

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Ultrasound guided percutaneous interventional therapy has been widely used in clinic. Aiming at the problem of soft tissue deformation caused by probe contact force in robot-assisted ultrasound-guided therapy, a real-time non-reference ultrasound image evaluation method considering soft tissue deformation is proposed. On the basis of ultrasound image brightness and sharpness, a multi-dimensional ultrasound image evaluation index was designed, which incorporated the aggregation characteristics of the organization. In order to verify the effectiveness of the proposed method, ultrasound images of four different models were collected for experiments, including prostate phantom, phantom with cyst, pig liver tissue, and pig liver tissue with cyst. In addition, the correlation between subjective and objective evaluations was analyzed based on Spearman’s rank correlation coefficient. Experimental results showed that the average evaluation time of a single image was 68.8 milliseconds. The evaluation time could satisfy real-time applications. The proposed method realizes the effective evaluation of real-time ultrasound image quality in robot-assisted therapy, and has good consistency with the evaluation of supervisors.

Citation： LI Yan, XIA Zeyang, WU Xiaojun, XIONG Jing. Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation. Journal of Biomedical Engineering, 2022, 39(3): 480-487. doi: 10.7507/1001-5515.202011077 Copy

1.	Abolhassani N, Patel R, Moallem M. Needle insertion into soft tissue: A survey. Med Eng Phys, 2007, 29(4): 413-431.
2.	Tang C, Xie G, Omisore O M, et al. A real-time needle tracking algorithm with first-frame linear structure removing in 2D ultrasound-guided prostate therapy// 2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019: 1240-1245.
3.	Hungr N, Baumann M, Long J A, et al. A 3-D ultrasound robotic prostate brachytherapy system with prostate motion tracking. IEEE T Robot, 2012, 28(6): 1382-1397.
4.	Dietrich O, Raya J G, Reeder S B, et al. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging, 2007, 26(2): 375-385.
5.	李国庆, 赵洋, 刘青萌, 等. 多层感知分解的全参考图像质量评估. 中国图象图形学报, 2019, 24(1): 149-158.
6.	Ma L, Li S, Zhang F, et al. Reduced-reference image quality assessment using reorganized DCT-based image representation. IEEE T Multimedia, 2011, 13(4): 824-829.
7.	魏乐松, 陈俊豪, 牛玉贞. 基于边缘和结构的无参考屏幕内容图像质量评估. 北京航空航天大学学报, 2019, 45(12): 2449-2455.
8.	Shiao Y H, Chen T J, Chuang K S, et al. Quality of compressed medical images. J Digit Imaging, 2007, 20(2): 149.
9.	Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity. IEEE T Image Process, 2004, 13(4): 600-612.
10.	周秋月. 基于CNN的无参考人脸图像质量评估. 武汉: 华中科技大学, 2017.
11.	Chow L S, Paramesran R. Review of medical image quality assessment. Biomed Signal Proces, 2016, 27: 145-154.
12.	Ye P, Doermann D. No-reference image quality assessment using visual codebooks. IEEE T Image Process, 2012, 21(7): 3129-3138.
13.	Saad M A, Bovik A C, Charrier C. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE T Image Process, 2012, 21(8): 3339-3352.
14.	Mortamet B, Bernstein M A, Jack C R Jr, et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med, 2009, 62(2): 365-372.
15.	Osadebey M, Pedersen M, Arnold D, et al. Image quality evaluation in clinical research: A case study on brain and cardiac MRI images in multi-center clinical trials. IEEE J Transl Eng Health Med, 2018, 6: 1800915.
16.	Pang Ke, Shi Zaifeng, Li Zhao. An image quality assessment index based on visual saliency and gradient amplitude for telemedicine application// 2017 4th International Conference on Information Science and Control Engineering. Changsha: IEEE, 2017: 172-176.
17.	Moghaddam R F, Cheriet M. AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recogn, 2012, 45(6): 2419-2431.
18.	崔少国, 刘东权. 使用二维小波收缩法去除弹性成像蠕虫噪声. 生物医学工程学杂志, 2011, 28(3): 460-464.
19.	Chaple Girish N, Daruwala R D, Gofane Manoj S, et al. Sobel operator based edge detection methods for real time uses on FPGA// 2015 International Conference on Technologies for Sustainable Development (ICTSD). Mumbai: IEEE, 2015: 1-4.
20.	Streijl R C, Winkler S, Hands D S. Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimedia Syst, 2016, 22(2): 213-227.
21.	Delcourt C, Cubeau J, Balkau B, et al. Limitations of the correlation coefficient in the validation of diet assessment methods. CODIAB-INSERM-ZENECA Pharma Study Group. Epidemiology, 1994, 5(5): 518-524.
22.	程淑, 桂林, 冀航. 主观评分的归一化算法及误差分析. 高等函授学报(自然科学版), 2007(5): 28-30.
23.	Taylor R. Interpretation of the correlation coefficient: a basic review. J Diagn Med Sonog, 1990, 6(1): 35-39.
24.	Gilchrist C L, Xia J Q, Setton L A, et al. High-resolution determination of soft tissue deformations using MRI and first-order texture correlation. IEEE Trans Med Imaging, 2004, 23(5): 546-553.
25.	Mosbech T H, Ersbøll B K, Christensen L B. Quantification and validation of soft tissue deformation// Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging. Proc. SPIE 7262. Lake Buena Vista: SPIE, 2009: 72621D.

1. Abolhassani N, Patel R, Moallem M. Needle insertion into soft tissue: A survey. Med Eng Phys, 2007, 29(4): 413-431.
2. Tang C, Xie G, Omisore O M, et al. A real-time needle tracking algorithm with first-frame linear structure removing in 2D ultrasound-guided prostate therapy// 2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019: 1240-1245.
3. Hungr N, Baumann M, Long J A, et al. A 3-D ultrasound robotic prostate brachytherapy system with prostate motion tracking. IEEE T Robot, 2012, 28(6): 1382-1397.
4. Dietrich O, Raya J G, Reeder S B, et al. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging, 2007, 26(2): 375-385.
5. 李国庆, 赵洋, 刘青萌, 等. 多层感知分解的全参考图像质量评估. 中国图象图形学报, 2019, 24(1): 149-158.
6. Ma L, Li S, Zhang F, et al. Reduced-reference image quality assessment using reorganized DCT-based image representation. IEEE T Multimedia, 2011, 13(4): 824-829.
7. 魏乐松, 陈俊豪, 牛玉贞. 基于边缘和结构的无参考屏幕内容图像质量评估. 北京航空航天大学学报, 2019, 45(12): 2449-2455.
8. Shiao Y H, Chen T J, Chuang K S, et al. Quality of compressed medical images. J Digit Imaging, 2007, 20(2): 149.
9. Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity. IEEE T Image Process, 2004, 13(4): 600-612.
10. 周秋月. 基于CNN的无参考人脸图像质量评估. 武汉: 华中科技大学, 2017.
11. Chow L S, Paramesran R. Review of medical image quality assessment. Biomed Signal Proces, 2016, 27: 145-154.
12. Ye P, Doermann D. No-reference image quality assessment using visual codebooks. IEEE T Image Process, 2012, 21(7): 3129-3138.
13. Saad M A, Bovik A C, Charrier C. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE T Image Process, 2012, 21(8): 3339-3352.
14. Mortamet B, Bernstein M A, Jack C R Jr, et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med, 2009, 62(2): 365-372.
15. Osadebey M, Pedersen M, Arnold D, et al. Image quality evaluation in clinical research: A case study on brain and cardiac MRI images in multi-center clinical trials. IEEE J Transl Eng Health Med, 2018, 6: 1800915.
16. Pang Ke, Shi Zaifeng, Li Zhao. An image quality assessment index based on visual saliency and gradient amplitude for telemedicine application// 2017 4th International Conference on Information Science and Control Engineering. Changsha: IEEE, 2017: 172-176.
17. Moghaddam R F, Cheriet M. AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recogn, 2012, 45(6): 2419-2431.
18. 崔少国, 刘东权. 使用二维小波收缩法去除弹性成像蠕虫噪声. 生物医学工程学杂志, 2011, 28(3): 460-464.
19. Chaple Girish N, Daruwala R D, Gofane Manoj S, et al. Sobel operator based edge detection methods for real time uses on FPGA// 2015 International Conference on Technologies for Sustainable Development (ICTSD). Mumbai: IEEE, 2015: 1-4.
20. Streijl R C, Winkler S, Hands D S. Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimedia Syst, 2016, 22(2): 213-227.
21. Delcourt C, Cubeau J, Balkau B, et al. Limitations of the correlation coefficient in the validation of diet assessment methods. CODIAB-INSERM-ZENECA Pharma Study Group. Epidemiology, 1994, 5(5): 518-524.
22. 程淑, 桂林, 冀航. 主观评分的归一化算法及误差分析. 高等函授学报(自然科学版), 2007(5): 28-30.
23. Taylor R. Interpretation of the correlation coefficient: a basic review. J Diagn Med Sonog, 1990, 6(1): 35-39.
24. Gilchrist C L, Xia J Q, Setton L A, et al. High-resolution determination of soft tissue deformations using MRI and first-order texture correlation. IEEE Trans Med Imaging, 2004, 23(5): 546-553.
25. Mosbech T H, Ersbøll B K, Christensen L B. Quantification and validation of soft tissue deformation// Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging. Proc. SPIE 7262. Lake Buena Vista: SPIE, 2009: 72621D.

Journal of Biomedical Engineering

Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content