264
Views
10
CrossRef citations to date
0
Altmetric
Articles

Lung Parenchyma Segmentation: Fully Automated and Accurate Approach for Thoracic CT Scan Images

ORCID Icon & ORCID Icon

References

  • G. D. Rubin, et al., “Pulmonary nodules on multi-detector row ct scans: Performance comparison of radiologists and computer-aided detection,” Radiology, Vol. 234, no. 1, pp. 274–83, Jan. 2005. doi: 10.1148/radiol.2341040589
  • M. E. O'Brien, “Lung cancer screening: Is there a future?,” Indian. J. Med. Paediatr. Oncol., Vol. 35, no. 4, pp. 249–52, Oct. 2014. doi: 10.4103/0971-5851.144984
  • D. R. Aberle, et al., “Reduced lung-cancer mortality with low-dose computed tomographic screening,” N. Engl. J. Med., Vol. 365, no. 5, pp. 395–409, Aug. 2011. doi: 10.1056/NEJMoa1102873
  • S. G. Armato and W. F. Sensakovic, “Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis,” Acad. Radiol., Vol. 11, no. 9, pp. 1011–21, Sep. 2004. doi: 10.1016/j.acra.2004.06.005
  • S. G. Armato, et al., “The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans,” Med. Phys., Vol. 38, no. 2, pp. 915–31, Feb. 2011. doi: 10.1118/1.3528204
  • K. Clark, et al., “The cancer imaging archive (TCIA): Maintaining and operating a public information repository,” J. Digit. Imaging., Vol. 26, no. 6, pp. 1045–57, Jul. 2013. doi: 10.1007/s10278-013-9622-7
  • S. G. Armato, et al., “Computerized detection of pulmonary nodules on CT scans,” Radiographics, Vol. 19, no. 5, pp. 1303–11, Sep. 1999. doi: 10.1148/radiographics.19.5.g99se181303
  • J. Pu, et al., “Adaptive border marching algorithm: Automatic lung segmentation on chest CT images,” Comput. Med. Imaging. Graph., Vol. 32, no. 6, pp. 452–62, Sep. 2008. doi: 10.1016/j.compmedimag.2008.04.005
  • G. De Nunzio, et al., “Automatic lung segmentation in CT images with accurate handling of the hilar region,” J. Digit. Imaging., Vol. 24, no. 1, pp. 11–27, Feb. 2011. doi: 10.1007/s10278-009-9229-1
  • S. P. Kumar and M. V. Latte, “Modified and optimized method for segmenting pulmonary parenchyma in ct lung images, based on fractional calculus and natural selection,” J. Intell. Syst., 2017. DOI:doi: 10.1515/jisys-2017-0028.
  • I. Sluimer, et al., “Computer analysis of computed tomography scans of the lung: A survey,” IEEE. Trans. Med. Imaging., Vol. 25, no. 4, pp. 385–405, Apr. 2006. doi: 10.1109/TMI.2005.862753
  • S. Shen, et al., “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy,” Comput. Biol. Med., Vol. 57, pp. 139–49, Feb. 2015. doi: 10.1016/j.compbiomed.2014.12.008
  • L. Hedlund, et al., “Two methods for isolating the lung area of a CT scan for density information,” Radiology, Vol. 144, no. 2, pp. 353–57, 1982. doi: 10.1148/radiology.144.2.7089289
  • Y. Wei, et al., “A fully automatic method for lung parenchyma segmentation and repairing,” J. Digit. Imaging., Vol. 26, no. 3, pp. 483–95, Oct. 2013. doi: 10.1007/s10278-012-9528-9
  • S. P. Kumar and M. V. Latte, “Fully automated segmentation of lung parenchyma using break and repair strategy,” J. Intell. Syst., 2017. doi: 10.1515/jisys-2017-0020.
  • W.-J. Choi and T.-S. Choi, “Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images,” Inf. Sci., Vol. 212, pp. 57–78, 2012. doi: 10.1016/j.ins.2012.05.008
  • J. P. Ko and M. Betke, “Chest CT: Automated nodule detection and assessment of change over time-preliminary experience,” Radiology, Vol. 218, no. 1, pp. 267–73, Jan. 2001. doi: 10.1148/radiology.218.1.r01ja39267
  • R. A. Jarvis, “On the identification of the convex hull of a finite set of points in the plane,” Inf. Process. Lett., Vol. 2, no. 1, pp. 18–21, Mar. 1973. doi: 10.1016/0020-0190(73)90020-3
  • P. Varshini, et al., “ An improved adaptive border marching algorithm for inclusion of juxtapleural nodule in lung segmentation of ct-images,” in Wireless Networks and Computational Intelligence, vol. 292, Communications in Computer and Information Science. Springer, 2012, pp. 230–5.
  • D. Y. Kim, et al., “Pulmonary nodule detection using chest CT images,” Acta. Radiol., Vol. 44, no. 3, pp. 252–7, May 2003. doi: 10.1034/j.1600-0455.2003.00061.x
  • J. Wang, et al., “Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images,” Int. J. Comput. Assist. Radiol. Surg., Vol. 11, no. 5, pp. 817–26, Dec. 2016. doi: 10.1007/s11548-015-1332-9
  • C. Shi, et al., “Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation,” Med. Image. Anal., Vol. 38, pp. 30–49, May 2017. doi: 10.1016/j.media.2017.02.008
  • S. Hu, et al., “Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images,” IEEE. Trans. Med. Imaging., Vol. 20, no. 6, pp. 490–8, Jun. 2001. doi: 10.1109/42.929615
  • S. M. B. Netto, et al., “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med., Vol. 42, no. 11, pp. 1110–21, Nov. 2012. doi: 10.1016/j.compbiomed.2012.09.003
  • R. C. Gonzalez and R. E. Woods. Digital Image Processing, 2nd ed., M. McDonald, Ed. Upper Saddle River, NJ: PrenticeMHall Inc., 2002.
  • S.-K. Im, et al., “An biometric identification system by extracting hand vein patterns,” J. Korean Phys. Soc., Vol. 38, no. 3, pp. 268–72, Mar. 2001.
  • J. D. Foley, et al., Computer Graphics: Principles and Practice, The Systems Programming Series, 2nd ed. Boston, MA: Addison-Wesley Longman, 1996.
  • 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., 2017. doi: 10.1515/jisys-2017-0144.

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.