Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 17

Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems

Automated lung tumor detection and diagnosis in CT Scans using texture feature analysis and SVM

, , ,

DOI: http://dx.doi.org/10.15439/2018F176

Citation: Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 17, pages 13–20 ()

Full text

Abstract. CT scans are an important tool in the diagnosis of lung tumors in medicine. This work presents an automated system for lung tumor diagnosis on CT scans. Scans are automatically segmented using marker-based watershed transformation, which enables to successfully segment hardly separable, lung wall adjunct tumors. The scans are further analyzed in a sliding window approach using Haralick features and a Support Vector Machine classifier to detect and classify benign and malignant tumors. This novel approach for classification was tested using the LUNGx Challenge dataset and achieved exceptional results while utilizing a minimal training set.

References

  1. S. Armato III, L. Hadjiiski, G. Tourassi, K. Drukker, M. Giger, F. Li, G. Redmond, K. Farahani, J. Kirby, and L. Clarke, “Spie-aapm-nci lung nodule classification challenge dataset,” Cancer Imaging Arch, 2015.
  2. L. A. Torre, R. L. Siegel, and A. Jemal, “Lung Cancer Statistics.” Springer, Cham, 2016, pp. 1–19. [Online]. Available: http://link.springer.com/10.1007/978-3-319-24223-1_1
  3. Cancer Research UK, “World cancer factsheet,” 2012. [Online]. Available: http://www.cancerresearchuk.org/sites/default/files/cs_report_world.pdf
  4. N. Zayed and H. A. Elnemr, “Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities,” International Journal of Biomedical Imaging, vol. 2015, pp. 1–7, oct 2015. [Online]. Available: http://www.hindawi.com/journals/ijbi/2015/267807/
  5. F. Han, H. Wang, G. Zhang, H. Han, B. Song, L. Li, W. Moore, H. Lu, H. Zhao, and Z. Liang, “Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules,” Journal of Digital Imaging, vol. 28, no. 1, pp. 99–115, feb 2015. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/25117512 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4305062 http://link.springer.com/10.1007/s10278-014-9718-8
  6. Q. Zhao, C.-Z. Shi, and L.-P. Luo, “Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes.” Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, vol. 26, no. 4, pp. 451–8, aug 2014. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/25232219 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4153941
  7. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, nov 1973. [Online]. Available: http://ieeexplore.ieee.org/document/4309314/
  8. S. G. Kulkarni and S. B. Bagal, “Techniques for Lung Cancer Nodule Detection: A Survey,” International Research Journal of Engineering and Technology, pp. 2395–56, 2015. [Online]. Available: https://irjet.net/archives/V2/i9/IRJET-V2I9323.pdf
  9. N. B. Bahadure, A. K. Ray, and H. P. Thethi, “Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM,” International Journal of Biomedical Imaging, vol. 2017, pp. 1–12, mar 2017. [Online]. Available: https://www.hindawi.com/journals/ijbi/2017/9749108/
  10. S. G. Armato, K. Drukker, F. Li, L. Hadjiiski, G. D. Tourassi, R. M. Engelmann, M. L. Giger, G. Redmond, K. Farahani, J. S. Kirby, and L. P. Clarke, “LUNGx Challenge for computerized lung nodule classification,” Journal of Medical Imaging, vol. 3, no. 4, p. 044506, dec 2016. [Online]. Available: http://medicalimaging.spiedigitallibrary. org/article.aspx?doi=10.1117/1.JMI.3.4.044506
  11. K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, and F. Prior, “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,” Journal of Digital Imaging, vol. 26, no. 6, pp. 1045–1057, dec 2013. [Online]. Available: http://link.springer.com/10.1007/s10278-013-9622-7
  12. G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.
  13. T. Joachims, 2008, http://svmlight.joachims.org/ Accessed: 2018-02-05.
  14. Https://github.com/locked-fg/JFeatureLib Accessed: 2018-02-05.