A Review on the Research Progress of the Computer-Aided Detection of Pulmonary Nodule_Journal of Biomedical Engineering

Authors：

ZHAOYu ¹ , LUWen ² , WANGYuanjun ¹ ,  NIEShengdong ¹

1. Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Department of Radiology, Taishan Medical College, Taian 271016, China;

Corresponding author：

NIEShengdong, Email: nsd4647@163.com

Keywords：

lung image; pulmonary nodule; computer-aided detection; performance evaluation

DOI：

10.7507/1001-5515.20140222

Video：

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Computer-aided detection (CAD) of pulmonary nodule technology can effectively assist the radiologist to enhance lung nodule detection efficiency and accuracy rate, so it can lay the foundation for the early diagnosis of lung cancer. In order to provide reference for the scholars and to develop the CAD technology, we in this paper review the technology research and development of CAD of the pulmonary nodules which is based on CT image in recent years both home and abroad. At the same time, we also analyse the advantages and shortcomings of different methods. Then we present the improvement direction for reference. According to the literature in recent years, there still has been large development space in CAD technology for pulmonary nodules. The establishment and improvement of the CAD system in each step would be of great scientific value.

Citation： ZHAOYu, LUWen, WANGYuanjun, NIEShengdong. A Review on the Research Progress of the Computer-Aided Detection of Pulmonary Nodule. Journal of Biomedical Engineering, 2014, 31(5): 1172-1177. doi: 10.7507/1001-5515.20140222 Copy

1.	聂生东,孙希文,陈兆学.基于CT图像的肺结节计算机辅助检测技术的研究进展[J].中国医学物理学杂志,2009,26(2):1075-1079.
2.	LEE S L A, KOUZANI A Z, HU E J. Automated detection of lung nodules in computed tomography images: a review[J]. Mach Vision Appl, 2012, 23(1): 151-163.
3.	DICIOTTI S, PICOZZI G, FALCHINI M, et al. 3D segmentation algorithm of small lung nodules in spiral CT images[J]. Biomedical, 2008, 12(1): 7-19.
4.	DE HOOP B, DE BOO D W, GIETEMA H A, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance[J]. Radiology, 2010, 257(2): 532-540.
5.	GOMATHI M. Automated CAD for lung nodule detection using CT scans[C]//International Conference on Data Storage and Data Engineering, Bangalore: 2010: 150-153.
6.	曹蕾.肺结节图像的分析与识别[D].广州:南方医科大学,2009:1-90.
7.	Lung Imaging Database Consortium (LIDC) [OL]. (2011-01-01)[2013-03-04].http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC.
8.	YANAGAWA M, HONDA O, KIKUYAMA A, et al. Pulmonary nodules: effect of adaptive statistical iterative Reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-comparison of performance between different-dose CT scans[J]. Eur J Radiol, 2012, 81(10): 2877-2886.
9.	ZHANG G M, XIN J, WU J, et al. CT image denoising using wavelet transform and dynamic fuzzy logic[C]//International Workshop on Intelligent Systems and Applications. Wuhan:2009: 1-4.
10.	HUA P F, SONG Q, SONKA M L, et al. Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:2072-2075.
11.	张林,何中市,张杰慧.快速边界行进算法:一种CT图像肺实质自动分割策略[J].计算机应用研究,2011,28(9):3556-3558.
12.	EL-BAZL A, NITZKEN M, VANBOGAERTL E, et al. A novel shape-based diagnostic approach for early diagnosis of lung nodules[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:137-140.
13.	AKARAPHAN J, NAGUL C, RAJALIDA L. Three dimensional lung nodule segmentation and estimation using thresholding on local thickness[C]//International Conference on Signal Processing Communication and Computing. Hong Kong: 2012: 593-596.
14.	TAKAHIRO M, TAKUMI T, SHINYA M, et al. Classification of lung nodules on temporal subtraction image based on statistical features and improvement of segmentation accuracy[J]. Automation and Systems, 2012, 1(12): 1814-1817.
15.	SOUSA J R, SILVA A C, DE PAIVA A C, et al. Methodology for automatic detection of lung nodules in computerized tomography images[J]. Comput Methods Programs Biomed, 2010, 98(1): 1-14.
16.	郭浩.基于MIP的CT二维检测[D].西安:西安科技大学,2010.
17.	LI Y, LIU J, MENG L J. Suspected pulmonary nodule detection algorithm based on morphology and gray entropy[J]. Comput Sci Aut Eng, 2011, 1(4): 103-108.
18.	WOOK J C, TAE S C. Computer-aided detection of pulmonary nodules using genetic programming[C]//17th International Conference on Image Processing. Hong Kong: 2010: 4353-4356.
19.	韩妍妍,冯筠,崔鑫,等.动态多分类器集成在肺结节辅助检测中的应用[J].计算机工程与应用,2012,48(2):218-221.
20.	LIU Y, YANG J Z, ZHAO D Z, et al. A method of pulmonary nodule detection utilizing multiple support vector[C]//International Conference on Computer Application and System Modeling Machines. Taiyuan: 2010:118-121.
21.	曹蕾,黎维娟,冯前进.基于LDA和SVM的肺结节CT图像自动检测与诊断[J].南方医科大学学报,2011,31(2):324-328.
22.	SARAH T N, HAMID A. Automated detection and classification of pulmonary nodules in 3D thoracic CT images[C]//International Conference on Systems Man and Cybernetics. Istanbul: 2010:3774-3779.
23.	AMAL F, ASEM A, JAMES G, et al. Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose ct scans of the chest[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:169-172.
24.	王倩,龚建平,宋恩民,等.CT图像中肺结节毛刺特征的计算机量化方法[J].华中科技大学学报:自然科学版,2011,39(7):26-29.
25.	WEI C S, YANG H Y, CHENG H C. Computer aided diagnosis for pulmonary nodule on low-dose computed tomography(LDCT)using density features[C]//8th International Conference on Computer Graphics Imaging and Visualization. Singapore: 2011:166-169.
26.	GUAN Y D, WANG Y, ZOU Y. Computer-aided detection for pulmonary nodules base on the morphological and spatial features[C]//International Conference on Biomedical Engineering and Computer Science. Wuhan: 2010:1-4.
27.	WANG C M, GUAN Y H, ZUO C T, et al. Value of the texture feature for solitary ulmonary nodules and mass lesions based on PET/CT[C]//4th International Conference on Bioinformatics and Biomedical Engineering. Chengdu: 2010:1-8.
28.	CHADDAD A, TANOUGAST C, DANDACHE A, et al. Extraction of haralick features from segmented texture multispectral bio-images for detection of colon cancer cells[C]//First International Conference on Informatics and Computational Intelligence. Bandung: 2011:55-59.
29.	TEMESGUEN M, RUSSELL C, STEVEN K R. A new computationally efficient CAD system for pulmonary nodule detectioin CT imagery[J]. Med Image Anal, 2010, 14(3): 390-406.
30.	廉策.肺CAD算法评估辅助工具的设计与实现[J].机械设计与制造,2010,1(9):266-268.

1. 聂生东,孙希文,陈兆学.基于CT图像的肺结节计算机辅助检测技术的研究进展[J].中国医学物理学杂志,2009,26(2):1075-1079.
2. LEE S L A, KOUZANI A Z, HU E J. Automated detection of lung nodules in computed tomography images: a review[J]. Mach Vision Appl, 2012, 23(1): 151-163.
3. DICIOTTI S, PICOZZI G, FALCHINI M, et al. 3D segmentation algorithm of small lung nodules in spiral CT images[J]. Biomedical, 2008, 12(1): 7-19.
4. DE HOOP B, DE BOO D W, GIETEMA H A, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance[J]. Radiology, 2010, 257(2): 532-540.
5. GOMATHI M. Automated CAD for lung nodule detection using CT scans[C]//International Conference on Data Storage and Data Engineering, Bangalore: 2010: 150-153.
6. 曹蕾.肺结节图像的分析与识别[D].广州:南方医科大学,2009:1-90.
7. Lung Imaging Database Consortium (LIDC) [OL]. (2011-01-01)[2013-03-04].http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC.
8. YANAGAWA M, HONDA O, KIKUYAMA A, et al. Pulmonary nodules: effect of adaptive statistical iterative Reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-comparison of performance between different-dose CT scans[J]. Eur J Radiol, 2012, 81(10): 2877-2886.
9. ZHANG G M, XIN J, WU J, et al. CT image denoising using wavelet transform and dynamic fuzzy logic[C]//International Workshop on Intelligent Systems and Applications. Wuhan:2009: 1-4.
10. HUA P F, SONG Q, SONKA M L, et al. Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:2072-2075.
11. 张林,何中市,张杰慧.快速边界行进算法:一种CT图像肺实质自动分割策略[J].计算机应用研究,2011,28(9):3556-3558.
12. EL-BAZL A, NITZKEN M, VANBOGAERTL E, et al. A novel shape-based diagnostic approach for early diagnosis of lung nodules[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:137-140.
13. AKARAPHAN J, NAGUL C, RAJALIDA L. Three dimensional lung nodule segmentation and estimation using thresholding on local thickness[C]//International Conference on Signal Processing Communication and Computing. Hong Kong: 2012: 593-596.
14. TAKAHIRO M, TAKUMI T, SHINYA M, et al. Classification of lung nodules on temporal subtraction image based on statistical features and improvement of segmentation accuracy[J]. Automation and Systems, 2012, 1(12): 1814-1817.
15. SOUSA J R, SILVA A C, DE PAIVA A C, et al. Methodology for automatic detection of lung nodules in computerized tomography images[J]. Comput Methods Programs Biomed, 2010, 98(1): 1-14.
16. 郭浩.基于MIP的CT二维检测[D].西安:西安科技大学,2010.
17. LI Y, LIU J, MENG L J. Suspected pulmonary nodule detection algorithm based on morphology and gray entropy[J]. Comput Sci Aut Eng, 2011, 1(4): 103-108.
18. WOOK J C, TAE S C. Computer-aided detection of pulmonary nodules using genetic programming[C]//17th International Conference on Image Processing. Hong Kong: 2010: 4353-4356.
19. 韩妍妍,冯筠,崔鑫,等.动态多分类器集成在肺结节辅助检测中的应用[J].计算机工程与应用,2012,48(2):218-221.
20. LIU Y, YANG J Z, ZHAO D Z, et al. A method of pulmonary nodule detection utilizing multiple support vector[C]//International Conference on Computer Application and System Modeling Machines. Taiyuan: 2010:118-121.
21. 曹蕾,黎维娟,冯前进.基于LDA和SVM的肺结节CT图像自动检测与诊断[J].南方医科大学学报,2011,31(2):324-328.
22. SARAH T N, HAMID A. Automated detection and classification of pulmonary nodules in 3D thoracic CT images[C]//International Conference on Systems Man and Cybernetics. Istanbul: 2010:3774-3779.
23. AMAL F, ASEM A, JAMES G, et al. Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose ct scans of the chest[C]//International Symposium on Biomedical Imaging:From Nano to Macro. Chicago: 2011:169-172.
24. 王倩,龚建平,宋恩民,等.CT图像中肺结节毛刺特征的计算机量化方法[J].华中科技大学学报:自然科学版,2011,39(7):26-29.
25. WEI C S, YANG H Y, CHENG H C. Computer aided diagnosis for pulmonary nodule on low-dose computed tomography(LDCT)using density features[C]//8th International Conference on Computer Graphics Imaging and Visualization. Singapore: 2011:166-169.
26. GUAN Y D, WANG Y, ZOU Y. Computer-aided detection for pulmonary nodules base on the morphological and spatial features[C]//International Conference on Biomedical Engineering and Computer Science. Wuhan: 2010:1-4.
27. WANG C M, GUAN Y H, ZUO C T, et al. Value of the texture feature for solitary ulmonary nodules and mass lesions based on PET/CT[C]//4th International Conference on Bioinformatics and Biomedical Engineering. Chengdu: 2010:1-8.
28. CHADDAD A, TANOUGAST C, DANDACHE A, et al. Extraction of haralick features from segmented texture multispectral bio-images for detection of colon cancer cells[C]//First International Conference on Informatics and Computational Intelligence. Bandung: 2011:55-59.
29. TEMESGUEN M, RUSSELL C, STEVEN K R. A new computationally efficient CAD system for pulmonary nodule detectioin CT imagery[J]. Med Image Anal, 2010, 14(3): 390-406.
30. 廉策.肺CAD算法评估辅助工具的设计与实现[J].机械设计与制造,2010,1(9):266-268.

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