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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18.	王光磊, 王鵬宇, 王中陽, 等. 基于自動隨機游走的 DSA 圖像分割算法. 激光雜志, 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.
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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, 王鵬宇, 韓業晨, 等. 基于 K-means 聚類與改進隨機游走算法的冠脈光學相干斷層圖像斑塊分割. 生物醫學工程學雜志, 2017, 34(6): 869-875.
26.	黑嘯吉, 蔣慧琴, 馬嶺, 等. 一種基于低劑量 CT 圖像的肺結節分割方法. 計算機應用研究, 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, 周濤, 陸惠玲, 等. 基于集成卷積神經網絡的肺部腫瘤計算機輔助診斷模型. 生物醫學工程學雜志, 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. 趙涓涓, 紀國華, 強彥, 等. 基于分層模版種子點的分水嶺分割孤立性肺結節的方法. 清華大學學報: 自然科學版, 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. 孫申申, 范立南, 康雁, 等. 基于改進主動形狀模型的含胸壁粘連型腫塊的肺區分割方法研究. 生物醫學工程學雜志, 2016, 33(5): 879-884.
11. 陳侃, 李彬, 田聯房. 基于模糊速度函數的活動輪廓模型的肺結節分割. 自動化學報, 2013, 39(8): 1257-1264.
12. 吉宏偉, 何江萍, 楊新. 基于層次上下文活動輪廓的三維 CT 肝臟圖像分割. 生物醫學工程學雜志, 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. 中國圖象圖形學報, 2013, 18(8): 1019-1030.
17. 范彥革, 劉旭敏. 各向異性擴散的研究. 計算機工程與應用, 2006, 42(29): 55-58, 68.
18. 王光磊, 王鵬宇, 王中陽, 等. 基于自動隨機游走的 DSA 圖像分割算法. 激光雜志, 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, 王鵬宇, 韓業晨, 等. 基于 K-means 聚類與改進隨機游走算法的冠脈光學相干斷層圖像斑塊分割. 生物醫學工程學雜志, 2017, 34(6): 869-875.
26. 黑嘯吉, 蔣慧琴, 馬嶺, 等. 一種基于低劑量 CT 圖像的肺結節分割方法. 計算機應用研究, 2017, 34(1): 290-294.

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