Automated Tissue Characterization of Intravascular Ultrasound Gray-scale Images_Journal of Biomedical Engineering

Authors：

 SUNZheng , WANGLixin , ZHOUYa

Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;

Corresponding author：

SUNZheng, Email: sunzheng_tju@163.com

Keywords：

intravascular ultrasound; tissue characterization; texture feature extraction; local binary pattern; Haar-like; Gentle Adaboost

DOI：

10.7507/1001-5515.20160049

Video：

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Automated characterization of different vessel wall tissues including atherosclerotic plaques, branchings and stents from intravascular ultrasound (IVUS) gray-scale images was addressed. The texture features of each frame were firstly detected with local binary pattern (LBP), Haar-like and Gabor filter in the present study. Then, a Gentle Adaboost classifier was designed to classify tissue features. The methods were validated with clinically acquired image data. The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy. Results indicated that the recognition accuracy of lipidic plaques reached 94.54%, while classification precision of fibrous and calcified plaques reached 93.08%. High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%, respectively.

Citation： SUNZheng, WANGLixin, ZHOUYa. Automated Tissue Characterization of Intravascular Ultrasound Gray-scale Images. Journal of Biomedical Engineering, 2016, 33(2): 287-294. doi: 10.7507/1001-5515.20160049 Copy

1.	JEGERE S, NARBUTE I, ERGLIS A. Use of intravascular imaging in managing coronary artery disease[J]. World J Cardiol, 2014, 6(6):393-404.
2.	CASSAR A, MATSUO Y, HERRMANN J, et al. Coronary atherosclerosis with vulnerable plaque and complicated lesions in transplant recipients:new insight into cardiac allograft vasculopathy by optical coherence tomography[J]. Eur Heart J, 2013, 34(33):2610-2617.
3.	ALBERTI M, BALOCCO S, GATTA C, et al.. Automatic bifurcation detection in coronary IVUS sequences[J]. IEEE Trans Biomed Eng, 2012, 59(4):1022-1031.
4.	GE Junbo. Current status of percutaneous coronary intervention of chronic total occlusion[J]. J Zhejiang Univ Sci B, 2012, 13(8):589-602.
5.	ROTGER D, RADEVA P, BRUINING N. Automatic detection of bioabsorbable coronary stents in IVUS images using a cascade of classifiers[J]. IEEE Trans Inf Technol Biomed, 2010, 14(2):535-537.
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7.	KAPLE R K, TSUJITA K, MAEHARA A, et al. Accuracy of stent measurements using ECG-gated grayscale intravascular ultrasound images:a validation study[J]. Ultrasound in Med & Biol, 2009, 35(8):1265-1270.
8.	CIOMPI F, BALOCCO S, CAUS C, et al. Stent shape estimation through a comprehensive interpretation of intravascular ultrasound images[C]//Proceedings of 16^th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Nagoya, Japan:2013, 16(Pt 2):345-352.
9.	SCHOENHAGEN P.轻松掌握血管内超声[M].刘茜蒨, 刘健, 陈芸, 译.北京:人民军医出版社, 2009.
10.	ESCALERA S, PUJOL O, MAURI J, et al. Intravascular ultrasound tissue characterization with sub-class error-correcting output codes[J]. J Signal Process Syst, 2009, 55(1-3):35-47.
11.	KATOUZIAN A, CARLIER S G, LAINE A F. Methods in atherosclerotic plaque characterization using intravascular ultrasound (IVUS) images and backscattered signal[M]//SURI J S, KATHURIA C, MOLINARI F. Atherosclerotic disease management. Berlin:Springer, 2010:121-152.
12.	KARAMALIS A, WEIN W, KLEIN T, et al. Ultrasound confidence maps using random walks[J]. Med Image Anal, 2012, 16(6):1101-1112.
13.	UCHINO E, TOKUNAGA K, TANAKA H, et al. IVUS-based coronary plaque tissue characterization using weighted multiple k-nearest neighbor[J]. Engineering Letters, 2012, 20(3):211-216.
14.	YAMADA R, OKURA H, KUME T, et al. A comparison between 40 MHz intravascular ultrasound iMap imaging system and integrated backscatter intravascular ultrasound[J]. J Cardiol, 2013, 61(2):149-154.
15.	SEABRA J C, CIOMPI F, PUJOL O, et al. Rayleigh mixture model for plaque characterization in intravascular ultrasound[J]. IEEE Trans Biomed Eng, 2011, 58(5):1314-1324.
16.	SEABRA J C, CIOMPI F, RADEVA P, et al. A rayleigh mixture model for IVUS imaging[M]//SANCHES J M, LAINE A F, SURI J S. Ultrasound imaging. New York:Springer US, 2012:25-47.
17.	KATOUZIAN A, KARAMALIS A, SHEET D, et al. Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology[J]. IEEE Trans Biomed Eng, 2012, 59(11):3039-3049.
18.	ATHANASIOU L S, KARVELIS P S, TSAKANIKAS V D, et al. A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images:comparison with virtual histology[J]. IEEE Trans Inf Technol Biomed, 2012, 16(3):391-400.
19.	GAO Zhifan, GUO Wei, LIU Xin, et al. Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images[J]. Plos One, 2014, 9(11):e109997.
20.	LIENHART R, MAYDT J. An extended set of Haar-like features for rapid object detection[C]//Proceedings of the IEEE International Conference on Image Processing. Rochester, New York, USA:2002, 1:900-903.
21.	VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2001). Kauai, Hawaii, USA:2001, 1:511-518.
22.	FREUND Y, SCHAPIRE R E. Experiments with a new boosting algorithm[C]//Proceedings of the Thirteenth International Conference on Machine Learning. Bari, Italy:1996:148-156.

1. JEGERE S, NARBUTE I, ERGLIS A. Use of intravascular imaging in managing coronary artery disease[J]. World J Cardiol, 2014, 6(6):393-404.
2. CASSAR A, MATSUO Y, HERRMANN J, et al. Coronary atherosclerosis with vulnerable plaque and complicated lesions in transplant recipients:new insight into cardiac allograft vasculopathy by optical coherence tomography[J]. Eur Heart J, 2013, 34(33):2610-2617.
3. ALBERTI M, BALOCCO S, GATTA C, et al.. Automatic bifurcation detection in coronary IVUS sequences[J]. IEEE Trans Biomed Eng, 2012, 59(4):1022-1031.
4. GE Junbo. Current status of percutaneous coronary intervention of chronic total occlusion[J]. J Zhejiang Univ Sci B, 2012, 13(8):589-602.
5. ROTGER D, RADEVA P, BRUINING N. Automatic detection of bioabsorbable coronary stents in IVUS images using a cascade of classifiers[J]. IEEE Trans Inf Technol Biomed, 2010, 14(2):535-537.
6. CIOMPI F, HUA R, BALOCCO S et al. Learning to detect stent struts in intravascular ultrasound[C]//Proceedings of 6^th International Conference on IbPRIA. Funchal, Madeira, Portugal:2013:575-583.
7. KAPLE R K, TSUJITA K, MAEHARA A, et al. Accuracy of stent measurements using ECG-gated grayscale intravascular ultrasound images:a validation study[J]. Ultrasound in Med & Biol, 2009, 35(8):1265-1270.
8. CIOMPI F, BALOCCO S, CAUS C, et al. Stent shape estimation through a comprehensive interpretation of intravascular ultrasound images[C]//Proceedings of 16^th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Nagoya, Japan:2013, 16(Pt 2):345-352.
9. SCHOENHAGEN P.轻松掌握血管内超声[M].刘茜蒨, 刘健, 陈芸, 译.北京:人民军医出版社, 2009.
10. ESCALERA S, PUJOL O, MAURI J, et al. Intravascular ultrasound tissue characterization with sub-class error-correcting output codes[J]. J Signal Process Syst, 2009, 55(1-3):35-47.
11. KATOUZIAN A, CARLIER S G, LAINE A F. Methods in atherosclerotic plaque characterization using intravascular ultrasound (IVUS) images and backscattered signal[M]//SURI J S, KATHURIA C, MOLINARI F. Atherosclerotic disease management. Berlin:Springer, 2010:121-152.
12. KARAMALIS A, WEIN W, KLEIN T, et al. Ultrasound confidence maps using random walks[J]. Med Image Anal, 2012, 16(6):1101-1112.
13. UCHINO E, TOKUNAGA K, TANAKA H, et al. IVUS-based coronary plaque tissue characterization using weighted multiple k-nearest neighbor[J]. Engineering Letters, 2012, 20(3):211-216.
14. YAMADA R, OKURA H, KUME T, et al. A comparison between 40 MHz intravascular ultrasound iMap imaging system and integrated backscatter intravascular ultrasound[J]. J Cardiol, 2013, 61(2):149-154.
15. SEABRA J C, CIOMPI F, PUJOL O, et al. Rayleigh mixture model for plaque characterization in intravascular ultrasound[J]. IEEE Trans Biomed Eng, 2011, 58(5):1314-1324.
16. SEABRA J C, CIOMPI F, RADEVA P, et al. A rayleigh mixture model for IVUS imaging[M]//SANCHES J M, LAINE A F, SURI J S. Ultrasound imaging. New York:Springer US, 2012:25-47.
17. KATOUZIAN A, KARAMALIS A, SHEET D, et al. Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology[J]. IEEE Trans Biomed Eng, 2012, 59(11):3039-3049.
18. ATHANASIOU L S, KARVELIS P S, TSAKANIKAS V D, et al. A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images:comparison with virtual histology[J]. IEEE Trans Inf Technol Biomed, 2012, 16(3):391-400.
19. GAO Zhifan, GUO Wei, LIU Xin, et al. Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images[J]. Plos One, 2014, 9(11):e109997.
20. LIENHART R, MAYDT J. An extended set of Haar-like features for rapid object detection[C]//Proceedings of the IEEE International Conference on Image Processing. Rochester, New York, USA:2002, 1:900-903.
21. VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2001). Kauai, Hawaii, USA:2001, 1:511-518.
22. FREUND Y, SCHAPIRE R E. Experiments with a new boosting algorithm[C]//Proceedings of the Thirteenth International Conference on Machine Learning. Bari, Italy:1996:148-156.

Journal of Biomedical Engineering

Automated Tissue Characterization of Intravascular Ultrasound Gray-scale Images

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