Lung nodule segmentation based on fuzzy c-means clustering and improved random walk algorithm_Journal of Biomedical Engineering

Authors：

LIU Ce , ZHANG Huaqi , WANG Hongrui , LI Yan ,  WANG Guanglei

College of Eletronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;

Corresponding author：

WANG Guanglei, Email: 513197133@qq.com

Keywords：

fuzzy c-means clustering; random walk; difficult pulmonary nodules; spatial distance; geodesic

DOI：

10.7507/1001-5515.201811056

Video：

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Abstract Full text Figures/Tables Video References Cited by

Accurate segmentation of pulmonary nodules is an important basis for doctors to determine lung cancer. Aiming at the problem of incorrect segmentation of pulmonary nodules, especially the problem that it is difficult to separate adhesive pulmonary nodules connected with chest wall or blood vessels, an improved random walk method is proposed to segment difficult pulmonary nodules accurately in this paper. The innovation of this paper is to introduce geodesic distance to redefine the weights in random walk combining the coordinates of the nodes and seed points in the image with the space distance. The improved algorithm is used to achieve the accurate segmentation of pulmonary nodules. The computed tomography (CT) images of 17 patients with different types of pulmonary nodules were selected for segmentation experiments. The experimental results are compared with the traditional random walk method and those of several literatures. Experiments show that the proposed method has good accuracy in the segmentation of pulmonary nodule, and the accuracy can reach more than 88% with segmentation time is less than 4 seconds. The results could be used to assist doctors in the diagnosis of benign and malignant pulmonary nodules and improve clinical efficiency.

Citation： LIU Ce, ZHANG Huaqi, WANG Hongrui, LI Yan, WANG Guanglei. Lung nodule segmentation based on fuzzy c-means clustering and improved random walk algorithm. Journal of Biomedical Engineering, 2019, 36(6): 978-985. doi: 10.7507/1001-5515.201811056 Copy

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16.	Li Cuifang, Nie Shengdong, Wang Yuanjun, et al. Segmentation of sub-solid pulmonary nodules base on improved fuzzy C-means clustering. 中国图象图形学报, 2013, 18(8): 1019-1030.
17.	范彦革, 刘旭敏. 各向异性扩散的研究. 计算机工程与应用, 2006, 42(29): 55-58, 68.
18.	王光磊, 王鹏宇, 王中阳, 等. 基于自动随机游走的 DSA 图像分割算法. 激光杂志, 2018, 39(4): 81-85.
19.	Lassen B C, Jacobs C, Kuhnigk J M, et al. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol, 2015, 60(3): 1307-1323.
20.	Yatziv L, Bartesaghi S, Sapiro G. O(N) implementation of the fast marching algorithm. J Computat Phys, 2006, 212: 393-399.
21.	Protiere A, Sapiro G. Interactive image segmentation via adaptive weighted distances. IEEE Trans Image Process, 2007, 16(4): 1046-1057.
22.	Prakash A, Hewko M D, Sowa M, et al. Detection of atherosclerotic plaque from optical coherence tomography images using texture-based segmentation. Sovremennye Tehnologii V Medicine, 2015, 7(1): 21-28.
23.	Messay T, Hardie R C, Tuinstra T R. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Med Image Anal, 2015, 22(1): 48-62.
24.	Wang Weisheng, Luo Jiawei, Yang Xuedong, et al. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Acad Radiol, 2015, 22(4): 488-495.
25.	王光磊, 王鹏宇, 韩业晨, 等. 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, 34(6): 869-875.
26.	黑啸吉, 蒋慧琴, 马岭, 等. 一种基于低剂量 CT 图像的肺结节分割方法. 计算机应用研究, 2017, 34(1): 290-294.

1. Chen W, Zheng R, Baade P D, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2. Kubota T, Jerebko A K, Dewan M, et al. Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal, 2011, 15(1): 133-154.
3. 王媛媛, 周涛, 陆惠玲, 等. 基于集成卷积神经网络的肺部肿瘤计算机辅助诊断模型. 生物医学工程学杂志, 2017, 34(4): 543-551.
4. Kim B C, Yoon J S, Choi J S, et al. Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection. Neural Netw, 2019, 115: 1-10.
5. Zhang Y, Wu S, Yu G, et al. A hybrid image segmentation approach using watershed transform and FCM// Fourth International Conference on Fuzzy Systems and Knowledge Discovery. Haikou: IEEE, 2007, 4: 2-6.
6. 赵涓涓, 纪国华, 强彦, 等. 基于分层模版种子点的分水岭分割孤立性肺结节的方法. 清华大学学报: 自然科学版, 2014, 54(7): 910-916.
7. Armato S G, Giger M L, Moran C J, et al. Computerized detection of pulmonary nodules on CT scans. Radiographics, 1999, 19(5): 1303-1311.
8. Okada K, Comaniciu D, Krishnan A. Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multi slice CT. IEEE Trans on Medical Imaging, 2005, 24(3): 409-423.
9. Kostis W J, Reevesa P, Yankelewitz D F, et al. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imag, 2003, 22(10): 1259-1274.
10. 孙申申, 范立南, 康雁, 等. 基于改进主动形状模型的含胸壁粘连型肿块的肺区分割方法研究. 生物医学工程学杂志, 2016, 33(5): 879-884.
11. 陈侃, 李彬, 田联房. 基于模糊速度函数的活动轮廓模型的肺结节分割. 自动化学报, 2013, 39(8): 1257-1264.
12. 吉宏伟, 何江萍, 杨新. 基于层次上下文活动轮廓的三维 CT 肝脏图像分割. 生物医学工程学杂志, 2014, 31(2): 405-412.
13. Shen W, Zhou M, Yang F, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn, 2017, 61: 663-673.
14. Winkels M, Cohen T S. Pulmonary nodule detection in CT scans with equivariant CNNs. Med Image Anal, 2019, 55: 15-26.
15. Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2006, 28(11): 1768-1783.
16. Li Cuifang, Nie Shengdong, Wang Yuanjun, et al. Segmentation of sub-solid pulmonary nodules base on improved fuzzy C-means clustering. 中国图象图形学报, 2013, 18(8): 1019-1030.
17. 范彦革, 刘旭敏. 各向异性扩散的研究. 计算机工程与应用, 2006, 42(29): 55-58, 68.
18. 王光磊, 王鹏宇, 王中阳, 等. 基于自动随机游走的 DSA 图像分割算法. 激光杂志, 2018, 39(4): 81-85.
19. Lassen B C, Jacobs C, Kuhnigk J M, et al. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol, 2015, 60(3): 1307-1323.
20. Yatziv L, Bartesaghi S, Sapiro G. O(N) implementation of the fast marching algorithm. J Computat Phys, 2006, 212: 393-399.
21. Protiere A, Sapiro G. Interactive image segmentation via adaptive weighted distances. IEEE Trans Image Process, 2007, 16(4): 1046-1057.
22. Prakash A, Hewko M D, Sowa M, et al. Detection of atherosclerotic plaque from optical coherence tomography images using texture-based segmentation. Sovremennye Tehnologii V Medicine, 2015, 7(1): 21-28.
23. Messay T, Hardie R C, Tuinstra T R. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Med Image Anal, 2015, 22(1): 48-62.
24. Wang Weisheng, Luo Jiawei, Yang Xuedong, et al. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Acad Radiol, 2015, 22(4): 488-495.
25. 王光磊, 王鹏宇, 韩业晨, 等. 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, 34(6): 869-875.
26. 黑啸吉, 蒋慧琴, 马岭, 等. 一种基于低剂量 CT 图像的肺结节分割方法. 计算机应用研究, 2017, 34(1): 290-294.

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