The segmentation of the intracoronary optical coherence tomography (OCT) images is the basis of the plaque recognition, and it is important to the following plaque feature analysis, vulnerable plaque recognition and further coronary disease aided diagnosis. This paper proposes an algorithm about multi region plaque segmentation based on kernel graph cuts model that realizes accurate segmentation of fibrous, calcium and lipid pool plaques in coronary OCT image, while boundary information has been well reserved. We segmented 20 coronary images with typical plaques in our experiment, and compared the plaque regions segmented by this algorithm to the plaque regions obtained by doctor's manual segmentation. The results showed that our algorithm is accurate to segment the plaque regions. This work has demonstrated that it can be used for reducing doctors' working time on segmenting plaque significantly, reduce subjectivity and differences between different doctors, assist clinician's diagnosis and treatment of coronary artery disease.
Citation：
ZHANGBo, YANGJianli, WANGGuanglei, WANGHongrui, LIUXiuling, HANYechen. Plaque region segmentation of intracoronary optical cohenrence tomography images based on kernel graph cuts. Journal of Biomedical Engineering, 2017, 34(1): 15-20. doi: 10.7507/1001-5515.201606010
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| 1. |
林霖, 刘映峰, 缪绯. 冠状动脉易损斑块的检测方法进展. 实用医学杂志, 2014(9): 1353-1355.
|
| 2. |
王守亮. 冠状动脉易损斑块评价方法研究进展. 山东医药, 2015(10): 100-102.
|
| 3. |
Athanasiou L S, Bourantas C V, Rigas G A, et al. Fully automated Calcium detection using optical coherence tomography//35th Annual International Coference of the IEEE EMBS. Osaka,Japan, 2013: 1430-1433.
|
| 4. |
WANG Z, Hiroyuki K, Hiram G B, et al. Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of athrosclerosis. SPIE-The International Society for Optical Engineering, 2011, 7889(1): 78890N-78890N-7.
|
| 5. |
Prakash A, Hewko M D, Sowa M, et al. Texture based segmentation method to detect atherosclerotic plaque from optical tomography images.// European Conference on Biomedical Optics VI. Munich, Germany, 2013.
|
| 6. |
Prakash A, Hewko M D, Sowa M, et al. Detection of atherosclerotic plaque from optical coherence tomography images using texture-based segmentation. Medical Technologies in Medicine/ Sovremennye,Tehnologii v Medicine, 2015, 7(1): 21-28.
|
| 7. |
王千, 王成, 冯振元, 等. K-means 聚类算法研究综述. 电子设计工程, 2012, 20(7): 21-24.
|
| 8. |
Ben Salah M, Mitiche A, Ben Ayed I. Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans Image Process, 2011, 20(2): 545-557.
|
| 9. |
Kubo T, XU Chenyang, WANG Zhao, et al. Plaque and thrombus evaluation by optical coherence tomography. Int J Cardiovasc Imaging, 2011, 27(2): 289-298.
|
| 10. |
Athanasiou L, Bourantas C, Rigas G, et al. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images. J Biomed Opt, 2014, 19(2): 026009.
|
- 1. 林霖, 刘映峰, 缪绯. 冠状动脉易损斑块的检测方法进展. 实用医学杂志, 2014(9): 1353-1355.
- 2. 王守亮. 冠状动脉易损斑块评价方法研究进展. 山东医药, 2015(10): 100-102.
- 3. Athanasiou L S, Bourantas C V, Rigas G A, et al. Fully automated Calcium detection using optical coherence tomography//35th Annual International Coference of the IEEE EMBS. Osaka,Japan, 2013: 1430-1433.
- 4. WANG Z, Hiroyuki K, Hiram G B, et al. Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of athrosclerosis. SPIE-The International Society for Optical Engineering, 2011, 7889(1): 78890N-78890N-7.
- 5. Prakash A, Hewko M D, Sowa M, et al. Texture based segmentation method to detect atherosclerotic plaque from optical tomography images.// European Conference on Biomedical Optics VI. Munich, Germany, 2013.
- 6. Prakash A, Hewko M D, Sowa M, et al. Detection of atherosclerotic plaque from optical coherence tomography images using texture-based segmentation. Medical Technologies in Medicine/ Sovremennye,Tehnologii v Medicine, 2015, 7(1): 21-28.
- 7. 王千, 王成, 冯振元, 等. K-means 聚类算法研究综述. 电子设计工程, 2012, 20(7): 21-24.
- 8. Ben Salah M, Mitiche A, Ben Ayed I. Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans Image Process, 2011, 20(2): 545-557.
- 9. Kubo T, XU Chenyang, WANG Zhao, et al. Plaque and thrombus evaluation by optical coherence tomography. Int J Cardiovasc Imaging, 2011, 27(2): 289-298.
- 10. Athanasiou L, Bourantas C, Rigas G, et al. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images. J Biomed Opt, 2014, 19(2): 026009.
Journal type citation(6)
| 1. | 范同乐，李艳，王光磊，王洪瑞，韩业晨. 基于FGCM的冠脉OCT图像纤维斑块分割算法. 激光杂志. 2020(02): 98-102 .  Baidu Scholar | |
| 2. | 王光磊，张学红，韩业晨，王洪瑞，李艳. 基于中智学理论的冠脉光学相干断层图像的全自动多区域分割. 生物医学工程学杂志. 2019(01): 59-67 . View this site | |
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| 4. | 孙雪松，王晓丽. 数据挖掘常用算法及其在医学大数据研究中的应用. 中国数字医学. 2018(03): 9-12 . Baidu Scholar | |
| 5. | 宋笑语，李艳，张花齐，卢倩，王光磊. 基于改进区域生长算法的冠脉OCT图像钙化斑块分割. 电子测量与仪器学报. 2018(12): 66-70 . Baidu Scholar | |
| 6. | 王光磊，王鹏宇，韩业晨，刘秀玲，李艳，卢倩. 基于K-means聚类与改进随机游走算法的冠脉光学相干断层图像斑块分割. 生物医学工程学杂志. 2017(06): 869-875 . View this site | |
Other types of references(8)
| 1. | 梁芹. 颅内动脉斑块HRMR图像分割及三维重建研究[D]. 西南科技大学. 2022. Baidu Scholar | |
| 2. | 崔国旗. 光学相干断层影像中冠脉血管内膜自动提取及斑块区域分割算法研究[D]. 河北大学. 2019. Baidu Scholar | |
| 3. | 张学红. 冠脉OCT图像中的斑块自动分割算法研究[D]. 河北大学. 2020. Baidu Scholar | |
| 4. | 严静. 颅内动脉粥样硬化斑块HRMR图像分割与三维重建[D]. 西南科技大学. 2019. Baidu Scholar | |
| 5. | 束磊. 基于深度学习的OCT心血管易损斑块识别[D]. 哈尔滨工业大学. 2018. Baidu Scholar | |
| 6. | 杨磊. 基于影像特征与物理特征的斑块组织类型识别[D]. 河北大学. 2019. Baidu Scholar | |
| 7. | 王鹏宇. 冠脉OCT图像分析算法的研究与实现[D]. 河北大学. 2018. Baidu Scholar | |
| 8. | 魏帅. 冠状动脉的物理建模与流体力学分析[D]. 河北大学. 2018. Baidu Scholar | |
