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Automatic segmentation of lung cancer cells with the new parameters by using methods of image processing and analysis

  • Przemysław JędrusikEmail author
  • Robert Koprowski
  • Ilona Bednarek
  • Zygmunt Wróbel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 623)

Abstract

Modern diagnostic methods allow to get multiple information regarding research material. This work focused on the development of an algorithm for automatically determining the correct number of cells. The developed tool allows the detection of cells as individual objects, searching for the objects significantly larger than the sought and checking if they were a combination of objects. The algorithm was based on additional parameters designated in its subsequent steps as well as their respective correcting claimed searched result. Analyzed a large number of images, it was found that there is a close relationship between the surface area of the cells, the degree of extension and the location and correct detection of objects that are neither a cluster of cells, and nothing significant image artifacts. The developed algorithm was written using Matlab software.

Keywords

image processing algorithms automatic lung cancer cell culture cell migration 

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References

  1. 1.
    Acharya, T., Ray, A.K.: Image Processing - Principles and Applications. Wiley InterScience; 2006Google Scholar
  2. 2.
    American Cancer Society. Global Cancer Facts & Figures 2nd Edition. Atlanta: American Cancer Society; 2011.Google Scholar
  3. 3.
    Angenent, S., Pichon, E., Tannenbaum, A.: Mathematical methods in medical image processing. Bulletin of the American mathematical society, 43, 365–396; 2006Google Scholar
  4. 4.
    Elizabeth Flate, John R. D. Stalvey: Motility of select ovarian cancer cell lines: Effect of extracellular matrix proteins and the involvement of PAK2. Int J Oncol. 2014 Oct; 45(4): 1401âĂŞ1411.Google Scholar
  5. 5.
    J. Ferlay, E. Steliarova-Foucher, J. Lortet-Tieulent, S. Rosso, J.W.W. Coebergh, H. Comber, D. Forman, F. Bray; Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012; European Journal of Cancer (2013) 49, 1374âĂŞ 1403Google Scholar
  6. 6.
    Jianping Peng, Ganesan Ramesh, Lin Sun, and Zheng Dong: Impaired Wound Healing in Hypoxic Renal Tubular Cells: Roles of Hypoxia-Inducible Factor-1 and Glycogen Synthase Kinase 3β/β-Catenin Signaling. J Pharmacol Exp Ther. 2012 Jan; 340(1): 176âĂŞ184.Google Scholar
  7. 7.
    Koprowski, R., Korzyńska, A., Zieleźnik, W., Wróbel, Z., Małyszek, J., Stępień, B.,Wójcik, W.: Influence of the measurement method of features in ultrasound images of the thyroid in the diagnosis of Hashimoto’s disease. BioMedical Engineering OnLine, 11:91 (2012)Google Scholar
  8. 8.
    Nilendu C Purandare, Venkatesh Rangarajan; Imaging of lung cancer: Implications on staging and management; Indian J Radiol Imaging. 2015 Apr-Jun; 25(2): 109âĂŞ120Google Scholar
  9. 9.
    Sauvola J., Pietikainen M.: Adaptive document binarization; Pattern Recognition 33(2), 2000, p. 225–236Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Przemysław Jędrusik
    • 1
    Email author
  • Robert Koprowski
    • 1
  • Ilona Bednarek
    • 2
  • Zygmunt Wróbel
    • 1
  1. 1.Department of Computer Biomedical Systems, Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland
  2. 2.Department of Biotechnology and Genetic Engineering, School of Pharmacy with the Division of Laboratory Medicine in SosnowiecMedical University of SilesiaSosnowiecPoland

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