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Lung Cancer Detection with FPCM and Watershed Segmentation Algorithms

  • N. Bhaskar
  • T. S. GanashreeEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Lung malignant growth drives the causes among disease related passing around the world. WHO information showed 1.69 million passing away in 2015. An early disease investigation can enhance the viability of therapy and also upgrades victim’s possibility of exist. The Precision of disease investigation, Rate and Computerization levels decides the achievement of CAD frameworks. In this paper, we worked with the few effectively existing frameworks and discovered the best methodology for recognition of tumors. This article talks about the division with FPCM and Watershed Transform calculations. Computer-aided design includes six stages – a. Image Acquisition, b. Image Pre-processing, c. Lung Region Extraction, d. Segmentation, e. Feature Extraction and f. Classification. Firstly, RGB picture is transfers to dark scale thus the picture clamour is a greater distanced from original picture. Next essential job is division which is performed by utilizing Watershed Transform display. Watershed method characterizes the dark scale picture. After division, highlight extraction is dissected by mean of the fragmented lung region lastly delineation of lung lumps classifies with the help of SVM method. By using this method we accomplished precision: 99% and the time are less than 2 s. The proposed frameworks were executed in MATLAB programming.

Keywords

FPCM Watershed Transform Segmentation SVM (Support Vector Machine) 

References

  1. 1.
    Armato SG, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med. Phys. 28(8):1552–1561. http://www.ncbi.nlm.nih.gov/pubmed/11548926
  2. 2.
    Armatur SC, Piraino D, Takefuji Y (1992) Optimization neural networks for the segmentation of magnetic resonance images. IEEE Trans. Med. Image 11(2):215–220. http://www.neuro.sfc.keio.ac.jp/publications/pdf/sundar.pdf
  3. 3.
    Cheran SC, Gargano G (2005) Computer aided diagnosis for lung CT using artificial life models. Proceeding of the seventh international symposium on symbolic and numeric algorithms for scientific computing, Sept 25–29, IEEE Computer Society, Romania. ISBN: 0-7695-2453-2, pp 329–332Google Scholar
  4. 4.
    Wiemker R, Rogalla P, Zwartkruis R, Blaffert T (2002) Computer aided lung nodule detection on high resolution CT data. Med. Image. Proc. SPIE, vol. 4684, pp 677–688. http://adsabs.harvard.edu/abs/2002SPIE.4684.677W
  5. 5.
    Gomathi M, Thangaraj P (2010) A computer aided diagnosis system for lung cancer detection using support vector machine. Am. J. Appl. Sci. 7(12):1532–1538, ISSN 1546-9239Google Scholar
  6. 6.
    El-Baz A, Gimel’farb G, Falk R, El-Ghar MA (2007) A new CAD system for early diagnosis of detected lung nodules. Proceeding of the IEEE international conference on image processing, Sept 16-Oct 19. San Antonio, TX. ISSN: 1522-4880, ISBN: 978-1-4244-1436-9, pp 461–464Google Scholar
  7. 7.
    Fiebich M, Wormanns D, Heindel W (2001) Improvement of method for computer-assisted detection of pulmonary nodules in CT of the chest. Proc. SPIE Med. Image Conf. 4322:702–709 Google Scholar
  8. 8.
    Ginneken BV, Romeny BM, Viergever MA (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans. Med. Imaging 20(12):1228–1241, ISSN: 0278-0062Google Scholar
  9. 9.
    Gomathi M, Thangaraj P (2010) A new approach to lung image segmentation using fuzzy possibilistic C-means algorithm. IJCSIS 7(3):222–228, ISSN: 1947 5500Google Scholar
  10. 10.
    Gurcan MN, Sahiner B, Petrick N, Chan H, Kazerooni EA et al (2002) Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29(11):2552–2558. http://www.ncbi.nlm.nih.gov/pubmed/12462722
  11. 11.
    Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H et al (1998). Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput. Med. Imaging Graph. 22(2):157–167. http://www.ncbi.nlm.nih.gov/pubmed/9719856
  12. 12.
    Penedo MG, Carreira MJ, Mosquera A, Cabello D (1998) Computer-aided diagnosis: a neural-network based approach to lung nodule detection. IEEE Trans. Med. Imaging 17(6):872–880, ISSN: 0278-0062Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VTU-RRC, Visvesvaraya Technological UniversityBelagaviIndia
  2. 2.CSE DepartmentCMR Technical CampusKandlakoyaIndia
  3. 3.Department of Telecommunication EngineeringDayanandasagar College of EngineeringBangaloreIndia

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