Skip to main content
SpringerLink
Log in

Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis

  • Conference paper
Computer Vision/Computer Graphics Collaboration Techniques (MIRAGE 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6930))

Abstract

In the present paper, we propose a novel recognition method of pulmonary nodules (possible lung cancers) in thoracic CT scans. Pulmonary nodules and blood vessels are represented by 3-D deformable spherical and cylindrical models. The validity of these object models are evaluated by the probability distributions that reflect the results of the statistical anatomical analysis of blood vessel trees in human lungs. The fidelity of the object models to CT scans are evaluated by five similarity measurements based on the differences in intensity distributions between the CT scans and templates produced from the object models. Through these evaluations, the posterior probabilities of hypotheses that the object models appear in the CT scans are calculated by use of the Bayes theorem. The nodule recognition is performed by the maximum a posterior estimation. Experimental results obtained by applying the proposed method to actual CT scans are shown.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Weir, H.K.: Annual report to the nation on the status of cancer, 1975-2000. Journal National Cancer Institute 95(17), 1276–1299 (2003)

    Article  Google Scholar 

  2. Tanaka, T., Yuta, K., Kobayashi, Y.: A study of false-negative case in mass-screening of lung cancer. Jay.J.Thor.Med. 43, 832–838 (1984)

    Google Scholar 

  3. Oda, J., Akita, S., Shimada, K.: A study of false-negative case in comparative reading of mass-screening of lung cancer. Lung Cancer 29, 271–278 (1989)

    Google Scholar 

  4. Yamamoto, S., Tanaka, I., Senda, M., Tateno, Y., Iinuma, T., Matsumoto, T., Matsumoto, M.: Image processing for computer-aided diagnosis of lung cancer by CT(LSCT). Systems and Computers in Japan 25(2), 67–80 (1994)

    Article  Google Scholar 

  5. Henschke, C.I., McCauley, D.I., Yankelevitz, D.F., Naidich, D.P., McGuinness, G., Miettinen, O.S., Libby, D.M., Pasmantier, M.W., Koizumi, J., Altorki, N.K., Smith, J.P.: Early lung cancer action project: overall design and findings from baseline screening. Lancet 354(9173), 99–105 (1999)

    Article  Google Scholar 

  6. van Ginneken, B.: Computer-aided diagnosis in thoracic computed tomography. Imaging Decisions 12(3), 11–22 (2009)

    Article  Google Scholar 

  7. Haralick, R.M., Sternberg, S.R., Xinhua, Z.: Imaging analysis using mathematical morphology. IEEE Trans. on Pattern Anal. and Machine Intell. 9(4), 532–550 (1987)

    Article  Google Scholar 

  8. Yamamoto, S., Matsumoto, M., Tateno, Y., Iinuma, T., Matshmoto, T.: Quoit filter: A new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in x-ray ct. In: Proc. 13th Int. Conf. Pattern Recognition II, pp. 3–7 (1996)

    Google Scholar 

  9. Okumura, T., Miwa, T., Kako, J., Yamamoto, S., Matsumoto, M., Tateno, Y., Iinuma, T., Matshmoto, T.: Variable n-quoit filter applied for automatic detection of lung cancer by x-ray ct. In: Computer Assisted Radiology and Surgery(CAR 1998), pp. 242–247 (1998)

    Google Scholar 

  10. Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.: Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images. IEEE Transactions on Medical Imaging 22(10), 1259–1274 (2003)

    Article  Google Scholar 

  11. Giger, M.L., Bae, K.T., MacMahon, H.: Computerized detection of pulmonary nodules in ct images. Investigative Radiology 29(4), 459–465 (1994)

    Article  Google Scholar 

  12. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kiknis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2(2), 143–168 (1998)

    Article  Google Scholar 

  13. Li, Q., Doi, K.: New selective nodule enhancement filter and its application for significant improvement of nodule detection on computed tomography. In: Proc. SPIE, vol. 5370 (2004)

    Google Scholar 

  14. McNitt-Gray, M.F., Wyckoff, N., Hart, E.M., Sayre, J.W., Goldin, J.G., Aberle, D.R.: Computer-aided techniques to characterize solitary pulmonary nodules imaged on ct. In: Computer-Aided Diagnosis in Medical Imaging, pp. 101–106. Elsevier, Amsterdam (1999)

    Google Scholar 

  15. Armato III, S.G., Giger, M.L., Moran, C.J., Doi, K., MacMahon, H.: Computerized detection of lung nodules on ct scans. RadioGraphics 19(5), 1303–1311 (1999)

    Article  Google Scholar 

  16. Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., Sone, S., Doi, K.: Computerized scheme for automated detection of lung nodules in low-dose ct images for lung cancer screening. Academic Radiology 11(6), 617–629 (2004)

    Article  Google Scholar 

  17. Matsumotoa, S., Kundelb, H.L., Geeb, J.C., Gefterb, W.B., Hatabu, H.: Pulmonary nodule detection in ct images with quantized convergence index filter. Medical Image Analysis 10(3), 343–352 (2006)

    Article  Google Scholar 

  18. Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Eguchi, K., Kaneko, M., Moriyama, N.: Quantitative surface characterization of pulmonary nodules based on thin-section ct images. IEEE Transaction Nuclear Science 45, 2132–2138 (1998)

    Article  Google Scholar 

  19. Suzuki, K., Armato, S.G., Li, F., Sone, S., Doi, K.: Massive training artificial neural network (mtann) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics 30(7), 1602–1617 (2003)

    Article  Google Scholar 

  20. Suzuki, K., Horiba, I., Sugie, N.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(12), 1582–1596 (2003)

    Article  Google Scholar 

  21. Bator, M., Chmielewski, L.J.: Elimination of linear structures as an attempt to improve the specificity of cancerous mass detection in mammograms. Advances in Soft Computing 45, 596–603 (2007)

    Article  Google Scholar 

  22. Lee, Y., Hara, T., Fujita, H., Itoh, S., Ishigaki, T.: Automated detection of pulmonary nodules in helical ct images based on an improved template-matching technique. IEEE Transactions on Medical Imaging 20(7), 595–604 (2001)

    Article  Google Scholar 

  23. Ozekes, S., Saman, O., Ucan, O.N.: Nodule detection in a lung region that’s segmented with using genetic cellular neural networks and 3d template matching with fuzzy rule based thresholding. Korean Journal of Radiology 9(1), 1–9 (2008)

    Article  Google Scholar 

  24. Farag, A.A., El-Baz, A., Gimel’farb, G., Falk, R.: Detection and recognition of lung abnormalities using deformable templates. In: Proceedings of 17th International Conference on Pattern Recognition 2004, vol. 3, pp. 738–741 (2004)

    Google Scholar 

  25. Takizawa, H., Yamamoto, S., Shiina, T.: Recognition of pulmonary nodules in thoracic ct scans using 3-d deformable object models of different classes. Algorithms, Molecular Diversity Preservation International (MDPI) 3(2), 125–144 (2010)

    Google Scholar 

  26. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, London (2008)

    Google Scholar 

  27. Takizawa, H., Nishizako, H.: Lung cancer detection from x-ray ct scans using discriminant filters and view-based support vector machine. The Journal of the Institute of Image Electronics Engineers of Japan 40(1), 59–66 (2011)

    Google Scholar 

  28. Zwiggelaar, R., Parr, T.C., Schumm, J.E., Hutt, I.W., Taylor, C.J., Astley, S.M., Boggis, C.: Model-based detection of spiculated lesions in mammograms. Medical Image Analysis 3(1), 39–62 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tsukuba, 305-8573, Japan

    Hotaka Takizawa & Shigeyuki Ishii

Authors
  1. Hotaka Takizawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shigeyuki Ishii
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. INRIA Rocquencourt, Domaine de Voluceau, 78153, Le Chesnay, France

    André Gagalowicz

  2. Ghent University, TELIN, St. -Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Wilfried Philips

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Takizawa, H., Ishii, S. (2011). Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science, vol 6930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24136-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24136-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24135-2

  • Online ISBN: 978-3-642-24136-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation