An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network_Journal of Biomedical Engineering

Authors：

ZHANG Huahai ¹ ,  BAI Peirui ¹ , GUO Ziyang ¹ , DU Linghao ¹ , LI Chang ¹ , REN Yande ² , YANG Kai ¹ , LIU Qingyi ¹

1. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P.R.China;
2. Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P.R.China;

Corresponding author：

BAI Peirui, Email: bprbjd@163.com

Keywords：

three-dimensional medical image segmentation; surfacelet transform; pulse coupled neural network; pulmonary parenchymal segmentation

DOI：

10.7507/1001-5515.201908060

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.

Citation： ZHANG Huahai, BAI Peirui, GUO Ziyang, DU Linghao, LI Chang, REN Yande, YANG Kai, LIU Qingyi. An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network. Journal of Biomedical Engineering, 2020, 37(4): 630-640. doi: 10.7507/1001-5515.201908060 Copy

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5.	孙运慧, 白培瑞, 张晓洁. 基于 Surfacelet 变换的三维 CT 图像边缘检测. 扬州大学学报:自然科学版, 2018, 21(3): 54-59.
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17.	Cai Bo, Ye Wei, Zhao Jianhui. A dynamic texture based segmentation method for ultrasound images with Surfacelet, HMT and parallel computing. Multimed Tools Appl, 2019, 78(5): 5381-5401.
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19.	Lu Y M, Do M N. Multidimensional directional filter banks and surfacelets. IEEE Trans Image Process, 2007, 16(4): 918-931.
20.	Wang Z, Ma Y, Cheng F, <italic>et al</italic>. Review of pulse-coupled neural networks. Image and Vision Computing, 2010, 28(1): 5-13.
21.	Ma Yide, Liu Qing, Quan Zhibai. Automated image segmentation using improved PCNN model based on cross-entropy//Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, IEEE, 2004: 743-746.
22.	Chen Yuli, Park S K, Ma Yide, <italic>et al</italic>. A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw, 2011, 22(6): 880-892.
23.	De Nunzio G, Tommasi E, Agrusti A, <italic>et al</italic>. Automatic lung segmentation in CT images with accurate handling of the hilar region. J Digit Imaging, 2011, 24(1): 11-27.
24.	Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//International conference on medical image computing and computer-assisted intervention, Cham: Springer, 2016: 424-432.
25.	武振宇, 白培瑞, 刘艺炜, 等. 基于模糊区域对比度增强的肺实质鲁棒分割. 生物医学工程研究, 2018, 37(2): 153-158.

1. 赵愉, 鲁雯, 王远军, 等. 肺结节计算机辅助检测技术研究概述. 生物医学工程学杂志, 2014, 31(5): 1172-1177.
2. Firmino M, Morais A H, Mendoça R M, <italic>et al</italic>. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online, 2014, 13(1): 41.
3. Tseng L Y, Huang L C. An adaptive thresholding method for automatic lung segmentation in CT images//AFRICON 2009, Nairobi: IEEE, 2009: 1-5.
4. Nóbrega R V M, Rodrigues M B, Rebouças Filho P P. Segmentation and visualization of the lungs in three dimensions using 3D region growing and visualization toolkit in CT examinations of the chest//IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2017: 397-402.
5. 孙运慧, 白培瑞, 张晓洁. 基于 Surfacelet 变换的三维 CT 图像边缘检测. 扬州大学学报:自然科学版, 2018, 21(3): 54-59.
6. Rebouças F P, Cortez P C, da Silva B A, <italic>et al</italic>. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal, 2017, 35: 503-516.
7. Mansoor A, Bagci U, Foster B, <italic>et al</italic>. Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radio Graphics, 2015, 35(4): 1056-1076.
8. 张文莉, 吕晓琪, 谷宇, 等. 基于肺部 CT 图像中肺实质分割的研究进展. 中国医学物理学杂志, 2017, 34(9): 902-907.
9. Nizami I F, Hasan S U, Javed I T. A wavelet frames + K-means based automatic method for lung area segmentation in multiple slices of CT scan//17th IEEE International Multi Topic Conference 2014. IEEE, 2014: 245-248.
10. Birkbeck N, Kohlberger T, Zhang J, et al. Lung segmentation from CT with severe pathologies using anatomical constraints//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2014: 804-811.
11. Zhou Hailing, Goldgof D B, Hawkins S, et al. A robust approach for automated lung segmentation in thoracic CT//2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2015: 2267-2272.
12. Kumar A, Agarwala S, Dhara A K, et al. Segmentation of lung field in HRCT images using U-Net based fully convolutional networks//Annual Conference on Medical Image Understanding and Analysis, Cham: Springer, 2018: 84-93.
13. Kamal U, Rafi A M, Hoque R, et al. Lung cancer tumor region segmentation using recurrent 3d-denseUNet. arXiv preprint, 2018. arXiv: 1812.01951.
14. Harrison A P, XU Z, George K, et al. Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2017: 621-629.
15. Ngo T A, Carneiro G. Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference//2015 IEEE International Conference on Image Processing (ICIP), IEEE, 2015: 2140-2143.
16. Cao Chensi, Liu Feng, Tan Hai, <italic>et al</italic>. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics, 2018, 16(1): 17-32.
17. Cai Bo, Ye Wei, Zhao Jianhui. A dynamic texture based segmentation method for ultrasound images with Surfacelet, HMT and parallel computing. Multimed Tools Appl, 2019, 78(5): 5381-5401.
18. Aggarwal N, Rana B, Agrawal R K. Role of surfacelet transform in diagnosing Alzheimer’s disease. Multidimens Syst Signal Process, 2019, 30(4): 1839-1858.
19. Lu Y M, Do M N. Multidimensional directional filter banks and surfacelets. IEEE Trans Image Process, 2007, 16(4): 918-931.
20. Wang Z, Ma Y, Cheng F, <italic>et al</italic>. Review of pulse-coupled neural networks. Image and Vision Computing, 2010, 28(1): 5-13.
21. Ma Yide, Liu Qing, Quan Zhibai. Automated image segmentation using improved PCNN model based on cross-entropy//Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, IEEE, 2004: 743-746.
22. Chen Yuli, Park S K, Ma Yide, <italic>et al</italic>. A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw, 2011, 22(6): 880-892.
23. De Nunzio G, Tommasi E, Agrusti A, <italic>et al</italic>. Automatic lung segmentation in CT images with accurate handling of the hilar region. J Digit Imaging, 2011, 24(1): 11-27.
24. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//International conference on medical image computing and computer-assisted intervention, Cham: Springer, 2016: 424-432.
25. 武振宇, 白培瑞, 刘艺炜, 等. 基于模糊区域对比度增强的肺实质鲁棒分割. 生物医学工程研究, 2018, 37(2): 153-158.

Journal of Biomedical Engineering

An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network

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