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Automatic Detection of Lung Cancer Nodules in Computerized Tomography Images

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Abstract

Lung cancer is the prime factor in cancer related deaths due to increasing rate of smoking and air pollution. The survival rate of cancer patients increases to 52% if it is localized, and decreases to 4% if it is metastasized. The existing system use simple thresholding approach and pattern recognition method to segment lung and identify nodules in the lung computerized tomography (CT) images. But quality of this process is affected by the image acquisition protocols, scanner types, and inhomogeneity of intensities in the lung region. In order to overcome the drawbacks of the previous process and for separation of nodules from non-nodules, there is a need for an automatic computer aided detection system. This paper proposes an automatic method for detecting nodules from patient lung CT images. In this automatic approach first step is a lung segmentation to differentiate lung and background and make the detection of nodule simple. Then the lung region is segmented by Contextual Clustering based region growing method. Vector quantization method is used to identify the nodules present in the lungs. False positive reduction is done by using various combination of rule based filtering, support vector machine, k-Nearest Neighbours and Random Forest classifier. The performance of this method is evaluated in terms of accuracy, sensitivity and specificity.

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References

  1. Siegel R, Naishadham D, Jemal A (2013) Cancer statistics. CA Cancer J Clin 63:11–30

    Article  PubMed  Google Scholar 

  2. Han H, Li L, Han F, Song B, Moore W, Liang Z (2015) Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J Biomed Health Inf 19:648–659

    Article  Google Scholar 

  3. Prabin A, Veerappan J (2014) Automatic segmentation of lung CT images by CC based region growing. J Theor Appl Inf Technol 68:63–69

    Google Scholar 

  4. Tong J, Ying W, Dong WC (2010) A lung cancer lesions detection scheme based on CT Image. In: 2nd IEEE international conference on signal processing systems (ICSPS), China, 5 July 2010, vol 1, pp 557–560

  5. Teramoto A, Fujita H (2013) Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 8:193–205

    Article  PubMed  Google Scholar 

  6. Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14:390–406

    Article  PubMed  Google Scholar 

  7. Sun S, Guo Y, Guan Y, Ren H, Fan L, Kan Y (2014) Juxta-vascular nodule segmentation based on flow entropy and geodesic distance. IEEE J Biomed Health Inf 18:1355–1362

    Article  Google Scholar 

  8. Song J, Yang C, Fan L, Wang K, Yang F, Liu S, Tian J (2016) Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging 35:337–353

    Article  PubMed  Google Scholar 

  9. Lee SLA, Kouzani AZ, Nasierding G, Hu EJ (2009) Pulmonary nodule classification aided by clustering. In: IEEE international conference systems, man and cybernetics, San Antonio, October 11–14 2009, pp 906–911

  10. Soltaninejad S, Keshani M, Tajeripour F (2012) Lung nodule detection by KNN classifier and active contour modelling and 3D visualization. In: The 16th CSI international symposium on artificial intelligence and signal processing, Shiraz, Fars, May 2–3, pp 440–445

  11. Jose D, Chitra M, Nirmal Kumar P (2015) Reliability improvement of partitioned VLSI systems for fault tolerance. Int J Appl Eng Res 10:18151–18165

    Google Scholar 

  12. Ma Z, Manuel JRS, Tavares RM, Jorge N (2009) A review on the current segmentation algorithms for medical images. In: 1st international conference on imaging theory and applications (IMAGAPP), ISBN: 978-989-8111-68-5, pp 135–140

  13. Ma Z, Tavares JM, Jorge RN, Mascarenhas T (2010) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235–246

    Article  Google Scholar 

  14. Oliveira RB, Filho ME, Ma Z, Papa JP, Pereira AS, João Manuel RS, Tavares. (2016) Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 131:127–141

    Article  PubMed  Google Scholar 

  15. Filho M, Ma Z, Tavares JM (2015) A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices. J Med Syst 39:177

    Article  PubMed  Google Scholar 

  16. Jodas DS, Pereira AS, João Manuel RS, Tavares (2016) A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46:1–14

    Article  Google Scholar 

  17. Filho PPR, Cortez PC, da Silva AC, Barros VH, Albuquerque C, João MR, Tavares S (2017) Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal 35:503–516

    Article  Google Scholar 

  18. Igor RS, Cortez VPC, Neto EC, Soares JM, de Albuquerque VHC, Manuel J, Tavares RS (2016) Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Programs Biomed 124:91–107

    Article  Google Scholar 

  19. Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, Cerello P, DeCarlo F, Falaschi F, Fantacci ME, Gargano G, Kasae P, Torsello M (2009) A novel multithreshold method for nodule detection in lung CT. Med Phys 36(8):3607–3618

    Article  PubMed  Google Scholar 

  20. Opfer R, Wiemker R (2007) Performance analysis for computer-aided lung nodule detection on LIDC data. In: Proceedings of SPIE, vol 6515, pp 1–9

  21. Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R (2011) Computer-aided detection of lung nodules via 3-D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys 38(4):1962–1971

    Article  PubMed  Google Scholar 

  22. Sahiner B, Hadjiiski LM, Chan HP, Shi J, Way T, Cascade PN, Kazerooni EA, Zhou C, Wei J (2007) The effect of nodule segmentation on the accuracy of computerized lung nodule detection on CT scans: comparison on a data set annotated by multiple radiologists. In: Proceedings of SPIE, vol 6514, pp 65140L-1–65140L-7

  23. Tan M, Deklerck R, Jansen B, Bister M, Cornelis J (2011) A novel computer-aided lung nodule detection system for CT images. Med Phys 38(10):5630–5645

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KCG College of Technology, Karapakkam, Chennai, Tamilnadu, 600097, India

    Deepa Jose

  2. Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, Tamilnadu, India

    A. Noufal Chithara, P. Nirmal Kumar & H. Kareemulla

Authors
  1. Deepa Jose
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  2. A. Noufal Chithara
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  3. P. Nirmal Kumar
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  4. H. Kareemulla
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Correspondence to Deepa Jose.

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Jose, D., Chithara, A.N., Nirmal Kumar, P. et al. Automatic Detection of Lung Cancer Nodules in Computerized Tomography Images. Natl. Acad. Sci. Lett. 40, 161–166 (2017). https://doi.org/10.1007/s40009-017-0549-2

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  • DOI: https://doi.org/10.1007/s40009-017-0549-2

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