208
Views
6
CrossRef citations to date
0
Altmetric
Articles

Threshold-Based New Segmentation Model to Separate the Liver from CT Scan Images

ORCID Icon, ORCID Icon & ORCID Icon

REFERENCES

  • H. Elias, and H. Bengelsdorf, “The structure of the liver of vertebrates,” Cells Tissues Organs, Vol. 14, no. 4, pp. 297–337, 1952. doi: 10.1159/000140715
  • S. R. Abdel-Misih, and M. Bloomston, “Liver anatomy,” Surgical Clinics, Vol. 90, no. 4, pp. 643–653, 2010.
  • Jump up to:a b c “Anatomy and physiology of the liver – Canadian Cancer Society”, Cancer, Archived from the original on 2015.
  • J. Wang, H. Cao, J. Z. Zhang, and Y. Qi, “Computational protein design with deep learning neural networks,” Sci. Rep., Vol. 8, no. 1, pp. 1–9, 2018.
  • H. Jiang, S. Li, and S. Li, “Registration-based organ positioning and joint segmentation method for liver and tumor segmentation,” BioMed Res. Int., Vol. 2018, pp. 11, 2018. Article ID: 8536854.
  • Z. Z. Wang, C. Zhang, T. Jiao, M. L. Gao, and G. Zou, “Fully automatic segmentation and three-dimensional reconstruction of the liver in CT images,” J. Healthc. Eng., Vol. 15, no. 11, pp. 1390–1403, 2018.
  • S. Zheng, B. Fang, L. Li, M. Gao, and Y. Wang, “A variational approach to liver segmentation using statistics from multiple sources,” Physics in Medicine & Biology, Vol. 63, no. 2, pp. 025024, 2018. doi: 10.1088/1361-6560/aaa360
  • A. Biswas, P. Bhattacharya, and S. P. Maity, “3D segmentation of liver and its lesions using optimized geometric contours,” Procedia. Comput. Sci., Vol. 133, pp. 240–247, 2018. doi: 10.1016/j.procs.2018.07.029
  • S. K. Siri, and M. V. Latte, “Combined endeavor of neutrosophic set and Chan-Vese model to extract accurate liver image from CT scan,” Comput. Methods Programs Biomed., Vol. 151, pp. 101–109, 2017. doi: 10.1016/j.cmpb.2017.08.020
  • S. K. Siri, and M. V. Latte, “A novel approach to extract exact liver image boundary from abdominal CT scan using neutrosophic set and fast marching method,” J. Intell. Syst., Vol. 28, no. 4, pp. 517–532, 2019. doi: 10.1515/jisys-2017-0144
  • S. K. Siri, and M. V. Latte, “Universal liver extraction algorithm: an improved Chan–vese model,” J. Intell. Syst., Vol. 29, no. 1, pp. 237–250, 2018. doi: 10.1515/jisys-2017-0362
  • J. A. Sethian. Level set methods and fast marching methods: evolving interfaces in computational geometry”, fluid mechanics, computer vision, and materials science,. Vol. 3. Berkeley, CA: Cambridge university press, 1999.
  • J. A. Sethian, “A fast marching level set method for monotonically advancing fronts,” Proc. Natl. Acad. Sci. U. S. A., Vol. 93, no. 4, pp. 1591–1595, 1996. doi: 10.1073/pnas.93.4.1591
  • J. A. Sethian, and A. Mihai Popovici, “3-D travel time computation using the fast marching method,” Geophysics, Vol. 64, no. 2, pp. 516–523, 1999. doi: 10.1190/1.1444558
  • H. Sun, J.-G. Sun, Z.-Q. Sun, F.-X. Han, Z.-Q. Liu, M.-C. Liu, Z.-H. Gao, and X.-L. Shi, “Joint 3D travel time calculation based on fast marching method and wavefront construction,” Appl. Geophys., Vol. 14, no. 1, pp. 56–63, 2017. doi: 10.1007/s11770-017-0611-3
  • Y. Sun, and S. Fomel. “Fast-marching eikonal solver in the tetragonal coordinates”, In SEG technical program expanded abstracts 1998, pp. 1949-1952, 1998.
  • L. Yatziv, A. Bartesaghi, and G. Sapiro, “O (N) implementation of the fast marching algorithm,” J. Comput. Phys., Vol. 212, no. 2, pp. 393–399, 2006. doi: 10.1016/j.jcp.2005.08.005
  • S. Garrido, M. Malfaz, and D. Blanco, “Application of the fast marching method for outdoor motion planning in robotics,” Rob. Auton. Syst., Vol. 61, no. 2, pp. 106–114, 2013. doi: 10.1016/j.robot.2012.10.012
  • Z. Z. Wang, “A new approach for segmentation and quantification of cells or nanoparticles,” IEEE Trans. Ind. Inf., Vol. 12, no. 3, pp. 962–971, 2016. doi: 10.1109/TII.2016.2542043
  • Z. Z. Wang, C. Zhang, T. Jiao, M. Gao, and G. Zou, “Fully automatic segmentation and three-dimensional Reconstruction of the liver in CT images,” J. Healthc. Eng., Vol. 2018, pp. 10, 2018. Article ID: 6797102.
  • Z. Z. Wang, “Monitoring of GMAW weld pool from the reflected laser lines for real-time control,” IEEE Trans. Ind. Inf., Vol. 10, no. 4, pp. 2073–2083, 2014. doi: 10.1109/TII.2014.2349360
  • Z. Z. Wang, “Image segmentation by combining the global and local properties,” Expert. Syst. Appl., Vol. 87, pp. 30–40, 2017. doi: 10.1016/j.eswa.2017.06.008
  • Z. Wang, “Determining the clustering centers by slope difference distribution,” IEEE. Access., Vol. 5, pp. 10995–11002, 2017. doi: 10.1109/ACCESS.2017.2715861
  • N. Otsu, “A threshold selection method from gray-level Histograms,” IEEE Trans. Syst. Man Cybern, Vol. 9, pp. 62–66, 1979. doi: 10.1109/TSMC.1979.4310076
  • T. Chan, and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process., Vol. 10, pp. 266–277, 2001. doi: 10.1109/83.902291
  • F. Meyer, “Topographic distance and watershed lines,” Signal. Processing., Vol. 38, pp. 113–125, July 1994. doi: 10.1016/0165-1684(94)90060-4
  • Y. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recognit., Vol. 29, pp. 1335–1346, 1996. doi: 10.1016/0031-3203(95)00169-7
  • V. Chalana, and Y. Kim, “A methodology for evaluation of boundary detection algorithms on medical images,” IEEE Trans. Med. Imaging, Vol. 16, pp. 642–652, 1997. doi: 10.1109/42.640755
  • J. Min, M. W. Powell, and K. W. Bowyer, December. Automated performance evaluation of range image segmentation. In Proceedings Fifth IEEE Workshop on Applications of Computer Vision, December 2000 pp. 163–168. IEEE.
  • K. W. Bowyer, and P. J. Phillips. “Empirical evaluation techniques”, in Computer Vision, IEEE Computer Society Press, 1998.
  • A. Fenster, and B. Chiu. “Evaluation of segmentation algorithms for medical imaging”, IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 7186-7189, 2006.
  • K. W. Bowyer. “Validation of medical image analysis techniques”, The Handbook of Medical Imaging, Bellingham Washington, 2000.
  • V. Chalana, and Y. Kim, “A methodology for evaluation of boundary detection algorithms on medical images,” IEEE Trans. Med. Imaging, Vol. 16, pp. 642–652, 1997. doi: 10.1109/42.640755
  • C. E. Metz, “ROC methodology in radiologic imaging,” Invest. Radiol., Vol. 21, pp. 720–733, 1986. doi: 10.1097/00004424-198609000-00009
  • B. J. McNeil, and J. A. Hanley, “Statistical approaches to analysis of receiver operating characteristic ROC curves,” Med. Decis. Making, Vol. 14, pp. 137–150, 1984. doi: 10.1177/0272989X8400400203

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.