Automatic Segmentation of Four Dimensional Computed Tomography of Lung Tumor Based on Star Shape Prior and Graph Cuts_Journal of Biomedical Engineering

Authors：

SHENZhengwen ^1,2 , GAOYuanyuan ^1,2 ,  ZHANGYu ^1,2

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China;

Corresponding author：

ZHANGYu, Email: yuzhang@smu.edu.cn

Keywords：

lung four dimensional computed tomography (4D-CT); automatic segmentation; graph cuts; motion estimate; star shape prior

DOI：

10.7507/1001-5515.20160050

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Lung four dimensional computed tomography (4D-CT) is of great value in tumor target localization and precise cancer radiotherapy. However, it is hard to segment tumors in 4D-CT data manually, since the data may contain a great number of slices with tumor. Meanwhile, auto-segmentation does not certainly guarantee the accuracy due to the complexity of images. Therefore, a new automatic segmentation technique based on Graph Cuts with star shape prior was proposed to increase automation and guarantee the accuracy of segmentation in our laboratory. Firstly, an object seed was selected in the image of initial phase and an initial target block was formed centering the selected seed. Then, the full search block-matching algorithm was adopted to obtain the most similar target block in the next phase and compute the motion field between them, and so on. Afterwards, the center seeds of each phase were obtained according to the motion fields, which would be set to the center point of star shape prior. Finally, tumors could be automatically segmented with Graph Cuts algorithm and star shape prior. Both qualitative and quantitative evaluation results showed that our approach could not only guarantee the accuracy of segmentation but also increase automation, compared with the traditional Graph Cuts algorithm.

Citation： SHENZhengwen, GAOYuanyuan, ZHANGYu. Automatic Segmentation of Four Dimensional Computed Tomography of Lung Tumor Based on Star Shape Prior and Graph Cuts. Journal of Biomedical Engineering, 2016, 33(2): 295-302. doi: 10.7507/1001-5515.20160050 Copy

1.	王瑾, 许峰, 周清华.肺癌流行病学研究进展[J].中国肺癌杂志, 2005, 8(5):395-400.
2.	刘亚军, 黄华.近十年来放射治疗技术与设备的进展[J].中国医疗设备, 2012, 27(6):9-10, 17.
3.	张书旭. 4D-CT重建及其在放疗中的应用研究[D].广州:南方医科大学, 2009.
4.	甄鑫, 周凌宏, 卢文婷, 等.改进Demons算法的验证及其在4D-CT轮廓线推衍中的应用[J].南方医科大学学报, 2010, 30(12):2619-2624.
5.	BOYKOV Y, JOLLY M P. Interactive organ segmentation using graph cuts[C]//Medical Image Computing and Computer Assisted Intervention, Pittsburgh, 2000:276-286.
6.	FUNKA L G, BOYKOV Y, FLORIN C, et al. Automatic heart isolation for CT coronary visualization using graph-cuts[C]//3rd IEEE International Symposium on Biomedical Imaging:From Nano to Macro, Arlington, Virginia, 2006:614-617.
7.	DE VOS L, STEGHERR M. Parameterizable VLSI architectures for the full-search block-matching algorithm[J]. Circuits and Systems, IEEE Transactions on, 1989, 36(10):1309-1316.
8.	BOYKOV Y, JOLLY M P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images[C]//8th IEEE International Conference on Computer Vision.Vancouver, 2001(1):105-112.
9.	BOYKOV Y, FUNKA-LEA G. Graph Cuts and efficient N-D image segmentation[J]. Int J Comput Vis, 2006, 70(2):109-131.
10.	郭秋菊.基于星形先验的图像自动化分割方法研究[D].武汉:华中师范大学, 2013.
11.	KOLMOGOROV V, ZABIH R. What energy functions can be minimized via graph cuts?[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(2):147-159.
12.	BOYKOV Y, KOLMOGOROV V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(9):1124-1137.
13.	BOYKOV Y, VEKSLER O, ZABIH R. Fast approximate energy minimization via graph cuts[J].IEEE Trans Pattern Anal Mach Intell, 2001, 23(11):1222-1239.
14.	VEKSLER O. Star shape prior for graph-cut image segmentation[M]. Berlin Heidelberg:Springer, 2008:454-467.
15.	CASTILLO R, CASTILLO E, GUERRA R, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets[J]. Phys Med Biol, 2009, 54(7):1849-1870.
16.	黄新生, 杨庆伟, 王亦平, 等.图像序列运动估计技术综述[J].计算机仿真, 2008, 25(5):180-184.
17.	BRUNIG M, NIEHSEN W. Fast full-search block matching[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(2):241-247.
18.	HAN Dong-feng, BAYOUTH J, SONG Qi, et al. Globally optimal tumor segmentation in PET-CT images:a Graph-Based co-segmentation method[C]//INFORMATION PROCESSING IN MEDICAL IMAGING, 6801.Kloster Irsee, Germany, 2011:245-256.
19.	HUTTENLOCHER D P, KLANDERMAN G A, RUCKLIDGE W J. Comparing images using the hausdorff distance[J]. IEEE Trans Pattern Anal Mach Intell, 1993, 15(9):850-863.

1. 王瑾, 许峰, 周清华.肺癌流行病学研究进展[J].中国肺癌杂志, 2005, 8(5):395-400.
2. 刘亚军, 黄华.近十年来放射治疗技术与设备的进展[J].中国医疗设备, 2012, 27(6):9-10, 17.
3. 张书旭. 4D-CT重建及其在放疗中的应用研究[D].广州:南方医科大学, 2009.
4. 甄鑫, 周凌宏, 卢文婷, 等.改进Demons算法的验证及其在4D-CT轮廓线推衍中的应用[J].南方医科大学学报, 2010, 30(12):2619-2624.
5. BOYKOV Y, JOLLY M P. Interactive organ segmentation using graph cuts[C]//Medical Image Computing and Computer Assisted Intervention, Pittsburgh, 2000:276-286.
6. FUNKA L G, BOYKOV Y, FLORIN C, et al. Automatic heart isolation for CT coronary visualization using graph-cuts[C]//3rd IEEE International Symposium on Biomedical Imaging:From Nano to Macro, Arlington, Virginia, 2006:614-617.
7. DE VOS L, STEGHERR M. Parameterizable VLSI architectures for the full-search block-matching algorithm[J]. Circuits and Systems, IEEE Transactions on, 1989, 36(10):1309-1316.
8. BOYKOV Y, JOLLY M P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images[C]//8th IEEE International Conference on Computer Vision.Vancouver, 2001(1):105-112.
9. BOYKOV Y, FUNKA-LEA G. Graph Cuts and efficient N-D image segmentation[J]. Int J Comput Vis, 2006, 70(2):109-131.
10. 郭秋菊.基于星形先验的图像自动化分割方法研究[D].武汉:华中师范大学, 2013.
11. KOLMOGOROV V, ZABIH R. What energy functions can be minimized via graph cuts?[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(2):147-159.
12. BOYKOV Y, KOLMOGOROV V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(9):1124-1137.
13. BOYKOV Y, VEKSLER O, ZABIH R. Fast approximate energy minimization via graph cuts[J].IEEE Trans Pattern Anal Mach Intell, 2001, 23(11):1222-1239.
14. VEKSLER O. Star shape prior for graph-cut image segmentation[M]. Berlin Heidelberg:Springer, 2008:454-467.
15. CASTILLO R, CASTILLO E, GUERRA R, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets[J]. Phys Med Biol, 2009, 54(7):1849-1870.
16. 黄新生, 杨庆伟, 王亦平, 等.图像序列运动估计技术综述[J].计算机仿真, 2008, 25(5):180-184.
17. BRUNIG M, NIEHSEN W. Fast full-search block matching[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(2):241-247.
18. HAN Dong-feng, BAYOUTH J, SONG Qi, et al. Globally optimal tumor segmentation in PET-CT images:a Graph-Based co-segmentation method[C]//INFORMATION PROCESSING IN MEDICAL IMAGING, 6801.Kloster Irsee, Germany, 2011:245-256.
19. HUTTENLOCHER D P, KLANDERMAN G A, RUCKLIDGE W J. Comparing images using the hausdorff distance[J]. IEEE Trans Pattern Anal Mach Intell, 1993, 15(9):850-863.

Previous Article
Automated Tissue Characterization of Intravascular Ultrasound Gray-scale Images
Next Article
Segmentation Method of Colour White Blood Cell Image Based on HSI Modified Space Information Fusion

Journal of Biomedical Engineering

Automatic Segmentation of Four Dimensional Computed Tomography of Lung Tumor Based on Star Shape Prior and Graph Cuts

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content