Lung Structure Classification Using 3D Geometric Measurements and SVM

  • João Rodrigo Ferreira da Silva Sousa
  • Aristófanes Corrêa Silva
  • Anselmo Cardoso de Paiva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In this paper, a set of three features for aiding classification of lung nodule bearing candidates based upon morphological characteristics is proposed. Metrics were validated using Support Vector Machine (SVM) technique as classifier. Preliminary results indicate the efficiency of the adopted measurements, taking into account the sensitivity and specificity high rates obtained from the studied samplings.

Keywords

Lung Nodule Classification Geometric Measures SVM 

References

  1. 1.
    Partnership, N.L.C.: Frequently asked questions (2007), Available at http://www.nationallungcancerpartnership.org
  2. 2.
    Bi, J., Periaswamy, S., Okada, K., Kubota, T., Fung, G., Salganicoff, M., Rao, R.B.: Computer aided detection via asymmetric cascade of sparse hyperplane classifiers. In: KDD 2006, pp. 837–844. ACM Press, New York (2006)CrossRefGoogle Scholar
  3. 3.
    Agam, G., Armato III, S.G., Wu, C.: Vessel tree reconstruction in thoracic ct scans with application to nodule detection. IEEE Trans. Med. Imaging 24, 486–499 (2005)CrossRefGoogle Scholar
  4. 4.
    Uppaluri, R., Hoffman, E., Sonka, M., Hartley, P., Hunninghake, G., Mclennan, G.: Computer recognition of regional lung disease patterns. American Journal of Respiratory and Critical Care Medicine 160, 648–654 (1999)Google Scholar
  5. 5.
    Korfiatis, P., Kalogeropoulou, C., Costaridou, I.: Computer aided detection of lung nodules in multislice computed tomography (2006)Google Scholar
  6. 6.
    Mousa, W.A.H., Khan, M.A.U.: Lung nodule classification utilizing support vector machines. In: ICIP (3), pp. 153–156 (2002)Google Scholar
  7. 7.
    Clunie, D.A.: DICOM Structered Reporting. PixelMed Publishing, Pennsylvania (2000)Google Scholar
  8. 8.
    Silva, A.C.: Algoritmos para Diagnostico Assistido de Nodulos Pulmonares Solitarios em Imagens de Tomografia Computadorizada. PhD thesis, PUC-Rio (2004)Google Scholar
  9. 9.
    Gurcan, M.N., Sahiner, B., Petrick, N., Chan, H.P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L.M.: Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system. Medical Physics 2552–2558 (2002)Google Scholar
  10. 10.
    Silva, A., Carvalho, P.C., Nunes, R., Gattass, M.: Algorithms for assisted diagnosis of solitary lung nodules in computerized tomography images. Technical Report TR-2004-02, IMPA - Visgraf Laboratory (2004)Google Scholar
  11. 11.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  12. 12.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • João Rodrigo Ferreira da Silva Sousa
    • 1
  • Aristófanes Corrêa Silva
    • 1
  • Anselmo Cardoso de Paiva
    • 1
  1. 1.Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MABrazil

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