A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting_Journal of Biomedical Engineering

Authors：

LIU Shiwei ,  WANG Yuanjun

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;

Corresponding author：

WANG Yuanjun, Email: yjusst@126.com

Keywords：

cone beam computed tomography; local Gaussian distribution fitting; edge detection; tooth segmentation

DOI：

10.7507/1001-5515.201709042

Video：

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Abstract Full text Figures/Tables Video References Cited by

Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar’s structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels’ variance and mean values, which improves the algorithm’s robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms’ results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.

Citation： LIU Shiwei, WANG Yuanjun. A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting. Journal of Biomedical Engineering, 2019, 36(2): 291-297, 305. doi: 10.7507/1001-5515.201709042 Copy

1.	汪葛, 王远军. 基于水平集的牙齿CT图像分割技术. 计算机应用, 2016, 36(03): 827-832.
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7.	Zhang W. Algorithms of 3D segmentation and reconstruction based on teeth CBCT images. Lecture Notes in Electrical Engineering, 2011, 100: 713-720.
8.	Gao Hui, Chae O. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognit, 2010, 43(7): 2406-2417.
9.	Gan Yangzhou, Xia Zeyang, Xiong Jing, et al. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys, 2015, 42(1): 14-27.
10.	Ji Dongxu, Ong S H, Foong K W. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images. Comput Biol Med, 2014, 50(1): 116-128.
11.	Hosntalab M, Zoroofi R A, Tehrani-Fard A A, et al. Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set. Int J Comput Assist Radiol Surg, 2008, 3(3/4): 257-265.
12.	王立新, 刘新新, 刘希云, 等. 基于区域自适应形变模型的CT图像牙齿结构测量方法研究. 生物医学工程学杂志, 2016, 33(2): 308-314.
13.	Gan Y, Xia Z, Xiong J, et al. Angled tooth segmentation from computerized tomography images// 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju Island, Korea: IEEE, 2017: 3413-3146.
14.	Wang Li, He Lei, Mishra A, et al. Active contours driven by local Gaussian distribution fitting energy. Signal Process, 2009, 89(12): 2435-2447.
15.	Chan T F, Vese L A. Active contours without edges. IEEE Trans Image Process, 2001, 10(2): 266-277.
16.	Osher S, Sethian J A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys, 1988, 79(1): 12-49.
17.	Li C, Kao C Y, Gore J C, et al. Implicit active contours driven by local binary fitting energy// 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, America: IEEE, 2007: 1-7.
18.	Chen Y T, Tseng D C. Medical image segmentation based on the Bayesian level set method// Medical Imaging and Informatics Second International Conference. Beijing, China: SpringerLink, 2007: 25-34.
19.	Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: A new variational formulation// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. California, America: IEEE, 2005: 430-436.
20.	Caselles V. Geometric models for active contours// Proceedings 1995 International Conference on Image Processing. Washington, America: IEEE, 1995: 9-12.

1. 汪葛, 王远军. 基于水平集的牙齿CT图像分割技术. 计算机应用, 2016, 36(03): 827-832.
2. Dufour A, Shinin V, Tajbakhsh S, et al. Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans Image Process, 2005, 14(9): 1396-1410.
3. Heo H. Segmentation of tooth in CT images for the 3D reconstruction of teeth. Spie Proceedings, 2004, 5298(2): 157-160.
4. Xia Zeyang, Gan Yangzhou, Chang Lichao, et al. Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth. Comput Methods Programs Biomed, 2017, 138: 1-12.
5. Xia Zeyang, Gan Yangzhou, Xiong Jing, et al. Crown segmentation from computed tomography images with metal artifacts. IEEE Signal Process Lett, 2016, 23(5): 678-682.
6. Akhoondali H, Zoroofi R A, Shirani G. Rapid automatic segmentation and visualization of teeth in CT-scan data. J Appl Sci, 2009, 9(11): 2031-2044.
7. Zhang W. Algorithms of 3D segmentation and reconstruction based on teeth CBCT images. Lecture Notes in Electrical Engineering, 2011, 100: 713-720.
8. Gao Hui, Chae O. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognit, 2010, 43(7): 2406-2417.
9. Gan Yangzhou, Xia Zeyang, Xiong Jing, et al. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys, 2015, 42(1): 14-27.
10. Ji Dongxu, Ong S H, Foong K W. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images. Comput Biol Med, 2014, 50(1): 116-128.
11. Hosntalab M, Zoroofi R A, Tehrani-Fard A A, et al. Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set. Int J Comput Assist Radiol Surg, 2008, 3(3/4): 257-265.
12. 王立新, 刘新新, 刘希云, 等. 基于区域自适应形变模型的CT图像牙齿结构测量方法研究. 生物医学工程学杂志, 2016, 33(2): 308-314.
13. Gan Y, Xia Z, Xiong J, et al. Angled tooth segmentation from computerized tomography images// 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju Island, Korea: IEEE, 2017: 3413-3146.
14. Wang Li, He Lei, Mishra A, et al. Active contours driven by local Gaussian distribution fitting energy. Signal Process, 2009, 89(12): 2435-2447.
15. Chan T F, Vese L A. Active contours without edges. IEEE Trans Image Process, 2001, 10(2): 266-277.
16. Osher S, Sethian J A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys, 1988, 79(1): 12-49.
17. Li C, Kao C Y, Gore J C, et al. Implicit active contours driven by local binary fitting energy// 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, America: IEEE, 2007: 1-7.
18. Chen Y T, Tseng D C. Medical image segmentation based on the Bayesian level set method// Medical Imaging and Informatics Second International Conference. Beijing, China: SpringerLink, 2007: 25-34.
19. Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: A new variational formulation// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. California, America: IEEE, 2005: 430-436.
20. Caselles V. Geometric models for active contours// Proceedings 1995 International Conference on Image Processing. Washington, America: IEEE, 1995: 9-12.

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A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting

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