Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm

QIU Shi; WEN Desheng; CUI Ying; FENG Jun

doi:10.1049/cje.2016.07.009

Volume 25 Issue 4

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(4): 711-718

QIU Shi, WEN Desheng, CUI Ying, et al., “Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 711-718, 2016, doi: 10.1049/cje.2016.07.009

Citation:

QIU Shi, WEN Desheng, CUI Ying, et al., “Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 711-718, 2016, doi: 10.1049/cje.2016.07.009

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Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm

doi: 10.1049/cje.2016.07.009

1.
Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China;
2.
University of Chinese Academy of Sciences, Beijing 100049, China;
3.
College of Equipment Engineering, Engineering University of Chinese Armed Police Force, Xi'an 710086, China;
4.
School of Information Science and Technology, Northwest University, Xi'an 710127, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61372046), and the Natural Science Foundation of Shannxi Province, China (No.2014JM8338).

Received Date: 2015-10-22
Rev Recd Date: 2015-12-27
Publish Date: 2016-07-10

Abstract

Abstract

To overcome low accuracy and high false positive of existing computer-aided lung nodules detection. We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory. The proposed scheme involves two parts which simulate human eyes' cognition features such as simplicity, integrity and classification. Firstly, lung region was segmented from lung Computed tomography (CT) sequences. Then local three-dimensional information was integrated into the Maximum intensity projection (MIP) images from axial, coronal and sagittal profiles. In this way, lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules. The experimental database includes fifty-three high resolution CT images contained lung nodules, which had been confirmed by biopsy. The experimental results show that, the accuracy rate of the proposed algorithm achieves 91.29%. The proposed framework improves performance and computation speed for computer aided nodules detection.
- Computer-aided detection (CAD),
- Lung nodules,
- Maximum intensity projection (MIP)

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References(19)

References

S. Takemura, X.H. Han, Y.W. Chen, et al., "Enhancement and detection of lung nodules with multiscale filters in CT images", International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, China, pp.717-720, 2008.

V.S. Kumar and P.S. Kumar, "Lung nodules detection by computer aided diagnosis using image processing", International Journal of Advance Research in Computer Science and Management Studies, Vol.2, No.4, pp.84-89, 2014.

A. Farag, J. Graham, S. Elshazly, et al., "Data-driven lung nodule models for robust nodule detection in chest CT", International Conference on Pattern Recognition, Istanbul, Turkey, pp.2588-2591, 2010.

N. Chen, G. Liu, Y. Liao, et al., "Research on computer-aided diagnosis of lung nodule", IEEE Workshop on Electronics, Computer and Applications, Ottawa, Ontario, Canada, pp.1019-1022, 2014.

S. Nie, L. Li, Y. Wang, et al., "A segmentation method for sub-solid pulmonary nodules based on fuzzy c-means clustering", 5th International Conference on Biomedical Engineering and Informatics (BMEI), Chongqing, China, pp.169-172, 2012.

A. El-Baz, A. Elnakib, M. Abou El-Ghar, et al., "Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans", International Journal of Biomedical Imaging, Vol.2013, Article ID 517632, 11 pages, 2013.

A. Coenen, O. Honda, E.J. van der Jagt, et al., "Computer-assisted solid lung nodule 3D volumetryon CT:Influence of scan mode and iterative reconstruction:A CT phantom study", Japanese Journal of Radiology, Vol.31, No.10, pp.677-684, 2013.

F. Han, H. Wang, B. Song, et al., "A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules", SPIE Medical Imaging. International Society for Optics and Photonics, Lake Buena Vista (Orlando Area), Florida, USA, pp.86702Z-1-86702Z-7,2013.

B. Chen, T. Kitasaka, H. Honma, et al., "Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images", International Journal of Computer Assisted Radiology and Surgery, Vol.7, No.3, pp.465-482, 2012.

A. Desolneux, L. Moisan and J.M. Morel, "To my taste, the authors' focus on crucial works in Gestalt psychology is absolutely adequate:They concentrate on Wertheimer and Metzger. What I appreciate most is the authors' position with regard to the fundamental question of perception:What is the nature of visual stimuli? I quote:Common sense tells us that a figure could not arise just by chance:We are sure"., Gestalt Theory, Vol.35, No.2, pp.183-206, 2013.

A. Tartar, N. Kilic and A. Akan, "A new method for pulmonary nodule detection using decision trees", 35th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, pp.7355-7359, 2013.

L. Huai, W. Yue, K.J. Ray Liu, et al., "Computerized radiographic mass detection-Part I:Lesion site selection by morphological enhancement and contextual segmentation", IEEE Transactions on Medical Images, Vol.20, No.4, pp.289-301, 2001.

S. Naple, G.D. Rubin and R.B. Jeffrey, "STS-MIP:A new reconstruction technique for the chest", Journal of Computer Assisted Tomography, Vol.17, No.5, pp.832-838, 1993.

K.H. Lee, H. Hong, S. Hahn, et al., "Summation or axial slab average intensity projection of abdominal thin-section CT datasets:Can they substitute for the primary reconstruction from raw projection data?", Journal of Digital Imaging, Vol.4, No.21, pp.422-432, 2008.

C.M. Wu, "Regularization otsu's thresholding method based on posterior probability entropy", Acta Electronica Sinica, Vol.41, No.12, pp.2474-2478, 2013. (In Chinese)

H.X. Zhou, W. Guo, Y. Wang, et al., "Adaptive distributed detection approach for pulmonary nodules", Chinese Journal of Scientific Instrument, Vol.10, No.10, pp.2312-2316, 2010.(in Chinese)

H. Ocak, "A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being", Journal of Medical Systems, Vol.37, No.2, pp.1-9, 2013.

L. Sun, Z.B. Wu, C. Feng, et al., "A novel two classifier fusion method for spectral-spatial hyperspectral classifition", Acta Electronica Sinica, Vol.43, No.11, pp.2210-2217, 2015. (In Chinese)

SIMBA Public Database, "International early lung cancer action project[DB/OL]", available at http://www.via.cornell.edu/lungdb.html, 2015-1-18.

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