基于水平集的牙齿牙槽骨图像分割

doi:10.11938/cjmr20202827

摘要/Abstract

摘要：

口腔锥形束计算机断层扫描（Cone Beam Computed Tomography，CBCT）图像中牙齿及牙槽骨的分割对骨性结构的三维重建提供了基础，是实现牙齿牙槽骨三维可视化的必要步骤.本文根据牙齿及牙槽骨特点，将一种改进的势阱函数与水平集模型结合，克服以往势阱函数在部分区域出现“停止演化”或“过快演化”的缺陷，并将其应用在对牙齿牙槽骨的分割当中.采用多次小方差高斯滤波叠加的方式对图像进行序贯滤波预处理，解决单一方差高斯滤波难以有效滤除CBCT图像中噪声的问题，为准确分割提供了条件；基于序列图像相邻两张图片中同一牙齿的轮廓变化不大这一特点，以当前层的分割结果作为下一层曲线演化的初始轮廓，使得用更少的迭代次数得到相同结果，从而提高分割速度.另外，本文还将该算法应用于口腔磁共振图像中，并成功对单颗牙齿进行了分割.

关键词: 水平集, 势阱函数, 图像分割, 牙齿牙槽骨

Abstract:

Segmentation of tooth and alveolar bone from the cone beam computed tomography (CBCT) images provides the basic data for the three-dimensional reconstruction and visualization of bone structure. In this paper, according to the characteristics of tooth and alveolar bone, an improved potential well function was combined with the level set model for segmentation of tooth and alveolar bone, overcoming the defects of 'stop evolution' or 'too fast evolution' that might occur with the use of conventional potential well functions. Since it is difficult to effectively filter out the noises in CBCT image with the single variance Gaussian filter, a multiple small variance Gaussian filter stack was used to preprocess the image. As the contours of the same tooth in adjacent images of the image sequence showed only little changes, the segmentation result of the current layer was taken as the initial contour of the curve evolution for the next layer to reduce the times of iteration and increase the speed of segmentation. In addition, the algorithm is also used to segment a single tooth in magnetic resonance image of oral cavity successfully.

Key words: level set, potential well function, image segmentation, tooth and alveolar bone

中图分类号:

TP391.3

石沁祎,闫方,杨阳,陈玥甫,林晓浪,王远军. 基于水平集的牙齿牙槽骨图像分割[J]. 波谱学杂志, 2021, 38(2): 182-193.

Qin-yi SHI,Fang YAN,Yang YANG,Yue-fu CHEN,Xiao-lang LIN,Yuan-jun WANG. Image Segmentation of Tooth and Alveolar Bone with the Level Set Model[J]. Chinese Journal of Magnetic Resonance, 2021, 38(2): 182-193.

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参考文献 17

1	OSHER S , SETHIAN J . Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations[J]. Comput Phys, 1988, 79 (1): 12- 49. doi: 10.1016/0021-9991(88)90002-2
2	GAO H , CHAE O . Individual tooth segmentation from CT images using level set method with shape and intensity prior[J]. Pattern Recogn, 2010, 43 (7): 2407- 2417.
3	ZHANG K H , SONG H H , ZHANG L . Active contours driven by local image fitting energy[J]. Pattern Recogn, 2010, 43 (4): 1199- 1206. doi: 10.1016/j.patcog.2009.10.010
4	WANG Y J , LIU S W , WANG G , et al. Accurate tooth segmentation with improved hybrid active contour model[J]. Phys Med Biol, 2018, 64 (1): 015012. doi: 10.1088/1361-6560/aaf441
5	LI C M , XU C Y , GUI C F , et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Trans Image Process, 2010, 19 (12): 3243- 3254. doi: 10.1109/TIP.2010.2069690
6	WANG G , WANG Y J . Development of teeth segmentation from computed tomography images using level set method[J]. Journal of Computer Applications, 2016, 36 (3): 827- 832. doi: 10.3969/j.issn.1001-3695.2016.03.042
	汪葛, 王远军. 基于水平集的牙齿CT图像分割技术[J]. 计算机应用, 2016, 36 (3): 827- 832. doi: 10.3969/j.issn.1001-3695.2016.03.042
7	GAO H , OKSAM CHAE O . Individual tooth segmentation from CT images using level set method with shape and intensity prior[J]. Pattern Recogn, 2010, 43 (7): 2406- 2417. doi: 10.1016/j.patcog.2010.01.010
8	RAN D M , YAN J Y , CUI Y Y , et al. Ultrasound image segmentation of thyroid gland based on bilateral filters-distance regularized level[J]. Journal of Biomedical Engineering Research, 2019, 38 (2): 170- 175.
	冉冬梅, 严加勇, 崔崤峣, 等. 基于双边滤波-距离正则化水平集演化算法的甲状腺超声图像分割[J]. 生物医学工程研究, 2019, 38 (2): 170- 175.
9	SUN C , XU Y L , BI D Y , et al. Distance regularised level set method using v-potential well function[J]. Computer Applications and Software, 2013, 30 (4): 271- 274. doi: 10.3969/j.issn.1000-386x.2013.04.078
	孙超, 许悦雷, 毕笃彦, 等. 采用V势阱函数的距离归一化水平集算法[J]. 计算机应用与软件, 2013, 30 (4): 271- 274. doi: 10.3969/j.issn.1000-386x.2013.04.078
10	WANG D , HE K , ZHANG X . The level set image segmentation on sequential filtering[J]. Journal of Sichuan University (Natural Science Edition), 2016, 53 (3): 518- 525.
	王丹, 何坤, 张旭. 基于序贯滤波的水平集图像分割[J]. 四川大学学报(自然科版), 2016, 53 (3): 518- 525.
11	ZHONG L , ZHOU Y F , ZHANG X F , et al. Image segmentation by level set evolution with region consistency constraint[J]. Applied Mathematics: A Journal of Chinese Universities, 2017, 32 (4): 422- 442. doi: 10.1007/s11766-017-3534-0
12	WEI C C , YI X M . An improved method for image segmentation based on DRLSE level set[J]. Journal of Graphics, 2019, 40 (5): 885- 891.
	魏晨晨, 羿旭明. 基于改进DRLSE水平集模型的图像分割[J]. 图学学报, 2019, 40 (5): 885- 891.
13	WENG G R , HE Z Y . Active contour model based on adaptive sign function[J]. Journal of Software, 2019, 30 (12): 3892- 3906.
	翁桂荣, 何志勇. 基于自适应符号函数的主动轮廓模型[J]. 软件学报, 2019, 30 (12): 3892- 3906.
14	CHEN Y M , TAGARE H D , THIRUVENKADAM S , et al. Using prior shapes in geometric active contours in a variational framework[J]. Int J Comput Vision, 2002, 50 (3): 315- 328. doi: 10.1023/A:1020878408985
15	SAHOO P K , SOLTANI S , WONG A K C , et al. A survey ofthresholding techniques[J]. Computer Vision, Grapgics, and Image Processing, 1988, 41 (2): 233- 260. doi: 10.1016/0734-189X(88)90022-9
16	WANG G , WANG Y J , LIU Y , et al. Fast and robust segmentation of individual tooth crown from cone beam computed tomography images[J]. J Med Imag Health In, 2017, 7 (2): 355- 363.
17	HEIMANN T , VAN G B , STYNER M A , et al. Comparison and evaluation of methods for liver segmentation from CT datasets[J]. IEEE Trans Med Imaging, 2009, 28 (8): 1251- 1265. doi: 10.1109/TMI.2009.2013851

	组别	1	2	3	4	5	6	7	8	9	10	平均值	标准差
本文算法	VOE/%	8.84	8.36	9.42	8.41	8.77	8.01	6.13	10.32	9.57	8.23	8.61	1.06
	运算时间/s	15.32	15.04	14.33	14.34	15.30	14.68	15.92	14.88	14.23	14.28	14.83	0.54
DRLSE	VOE/%	16.02	16.88	18.32	15.60	9.66	15.50	8.73	18.23	16.53	13.77	14.92	3.13
	运算时间/s	16.65	16.23	15.99	16.77	15.34	14.67	15.66	17.33	18.19	14.83	16.16	1.05

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