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International Journal of Biomedical Imaging
Volume 2013, Article ID 517632, 11 pages
Research Article

Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans

1Bioimaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
2Urology and Nephrology Department, University of Mansoura, Mansoura 35516, Egypt
3Department of Computer Science, The University of Auckland 1142, Auckland, New Zealand
4Medical Imaging Division, Jewish Hospital, Louisville, KY 40202, USA
5Electrical and Computer Engineering Department, University of Louisville, KY 40292, USA

Received 9 September 2012; Revised 13 December 2012; Accepted 14 December 2012

Academic Editor: Kazunori Okada

Copyright © 2013 Ayman El-Baz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Mustafa Alam, Ganesh Sankaranarayanan, and Venkat Devarajan, “Lung Nodule Detection and Segmentation Using a Patch-Based Multi-Atlas Method,” 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 23–28, . View at Publisher · View at Google Scholar
  • Ahmed Soliman, Fahmi Khalifa, Amir Alansary, Georgy Gimel'farb, and Ayman El-Baz, “Performance evaluation of an automatic MGRF-based lung segmentation approach,” AIP Conference Proceedings, vol. 1559, pp. 323–332, 2013. View at Publisher · View at Google Scholar
  • Hao Han, Lihong Li, Huafeng Wang, Hao Zhang, William Moore, and Zhengrong Liang, “A novel computer-aided detection system for pulmonary nodule identification in CT images,” Medical Imaging 2014: Computer-Aided Diagnosis, vol. 9035, 2014. View at Publisher · View at Google Scholar
  • Saleem Iqbal, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum, “Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images,” Computational and Mathematical Methods in Medicine, vol. 2014, pp. 1–7, 2014. View at Publisher · View at Google Scholar
  • Sheeraz Akram, Muhammad Younus Javed, Ayyaz Hussain, Farhan Riaz, and M. Usman Akram, “Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques,” Journal of Experimental & Theoretical Artificial Intelligence, pp. 1–15, 2015. View at Publisher · View at Google Scholar
  • Cui Ying, Feng Jun, Qiu Shi, and Wen Desheng, “Lung nodules detection in CT images using gestalt-based algorithm,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 711–718, 2016. View at Publisher · View at Google Scholar
  • Shi Qiu, De-Sheng Wen, Jun Feng, and Ying Cui, “A new strategy lung nodules detection algorithm,” Tien Tzu Hsueh Pao/Acta Electronica Sinica, vol. 44, no. 6, pp. 1413–1419, 2016. View at Publisher · View at Google Scholar
  • Igor Rafael S. Valente, Paulo Cesar Cortez, Edson Cavalcanti Neto, Jose Marques Soares, Victor Hugo C. de Albuquerque, and Joao Manuel R. S. Tavares, “Automatic 3D pulmonary nodule detection in CT images: A survey,” Computer Methods And Programs In Biomedicine, vol. 124, pp. 91–107, 2016. View at Publisher · View at Google Scholar
  • Doaa Mousa, Nourhan Zayed, and Mahmoud Fakhr, “Significant Features To Detect Pulmonary Nodules From Ct Lung Images,” Biomedical Engineering: Applications, Basis and Communications, vol. 29, no. 06, pp. 1750045, 2017. View at Publisher · View at Google Scholar
  • Muhammad Zia ur Rehman, Muzzamil Javaid, Syed Irtiza Ali Shah, Syed Omer Gilani, Mohsin Jamil, and Shahid Ikramullah Butt, “An appraisal of nodules detection techniques for lung cancer in CT images,” Biomedical Signal Processing and Control, vol. 41, pp. 140–151, 2018. View at Publisher · View at Google Scholar