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Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs

Journal of Medical Systems volume 39, Article number: 46 (2015) Cite this article

Abstract

Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.

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References

  1. World Health Organization, World Health Report. http://www.who.int/whr/2004/en/. Accessed 12 Feb 2014, 2004.

  2. Lee, S. L. A., Kouzani, A. Z., and Hu, E. J., Automated detection of lung nodules in computed tomography images: A review. Mach. Vis. Appl. 23(1):151–163, 2012. doi:10.1007/s00138-010-0271-2.

    Article  Google Scholar 

  3. Edelsbrunner, H., Kirkpatrick, D. G., and Seidel, R., On the shape of a set of points in the plane. IEEE Trans. Inf. Theory 29(4):551–559, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  4. Wang, Q., Zhu, W., and Wang, B., Three-dimensional SVM with latent variable: Application for detection of lung lesions in CT images. J. Med. Syst. 39(1):171, 2015. doi:10.1007/s10916-014-0171-5.

    Article  Google Scholar 

  5. Avci, E., A new expert system for diagnosis of lung cancer: GDA—LS_SVM. J. Med. Syst. 36(3):2005–2009, 2012. doi:10.1007/s10916-011-9660-y.

    Article  Google Scholar 

  6. Armato, S. G., 3rd, McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., van Beek, E. J. R., Yankelevitz, D., et al., The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Med. Phys. 38(2):915–931, 2011.

    Article  Google Scholar 

  7. Cornell University, Public lung database to address drug response. Vision and Image Analysis Group (VIA) and International Early Lung Cancer Action Program (I-ELCAP) Labs. http://www.via.cornell.edu/crpf.html, Accessed 18 Feb 2014, 2008

  8. Ezoe, T., Takizawa, H., Yamamoto, S., Shimuzu, A., Matsumoto, T., Tateno, Y., Iimura, T., Matsumoto, M., An automatic detection method of lung cancers including ground glass opacities from chest X-ray CT images. In: Proc. of SPIE 4684:1672–1680, 2002.

  9. Frangi, A. F., Niessen, W. J., Hoogeveen, R. M., Walsum, T. V., and Viergever, M. A., Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans. Med. Imaging 18(10):946–956, 1999.

    Article  Google Scholar 

  10. Suzuki, K., Supervised “lesion-enhancement” filter by use of a Massive-Training Artificial Neural Network (MTANN) in Computer-Aided Diagnosis (CAD). Phys. Med. Biol. 54(18):31–45, 2009.

    Article  Google Scholar 

  11. Ochs, R. A., Goldin, J. G., Abtin, F., Kim, H. J., Brown, K., Batra, P., Roback, D., McNitt-Gray, M. F., and Brown, M. S., Automated classification of lung bronchovascular anatomy in CT using Adaboost. Med. Image Anal. 11(3):315–324, 2007.

    Article  Google Scholar 

  12. Paik, D. S., Beaulieu, C. F., Rubin, G. D., Acar, B., Jeffrey, R. B., Yee, J., Dey, J., and Napel, S., Surface normal overlap: A computer-aided detection algorithm with application. IEEE Trans. Med. Imaging 23(6):661–675, 2004.

    Article  Google Scholar 

  13. Retico, A., Delogu, P., Fantacci, M. E., Gori, I., and Martinez, A. P., Lung nodule detection in low-dose and thin-slice computed tomography. Comput. Biol. Biomed. 38(4):525–534, 2008.

    Article  Google Scholar 

  14. Li, Q., Li, F., and Doi, K., Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad. Radiol. 15(2):165–175, 2008.

    Article  MATH  Google Scholar 

  15. Armato, S. G., 3rd, Giger, M. I., Moran, C. J., Blackburn, J. T., Doi, K., and Macmahon, H., Computerized detection of pulmonary nodules on CT scans. Radiographics 19(5):1303–1311, 1999.

    Article  Google Scholar 

  16. Hu, S., Hoffman, E. A., and Reinhardt, J. M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging 20(6):490–498, 2001.

    Article  Google Scholar 

  17. El-Baz, A., Beache, G. M., Gimel’farb, G., Suzuki, K., Okada, K., Elnakib, A., Soliman, A., Abdollahi, B., Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int. J. Biomed. Imaging Article ID 942353, 46 pages, 2013

  18. Pu, J., Roos, J., Yi, C. A., Napel, S., Rubin, G. D., and Paik, D. S., Adaptive border marching algorithm: Automatic lung segmentation on chest CT images. Comput. Med. Imaging Graph. 32(6):452–462, 2008.

    Article  Google Scholar 

  19. Sensakovic, W. F., Starkey, A., Armato, S. G. 3rd, A general method for the identification and repair of concavities in segmented medical images. IEEE Nuclear Science Symposium Conference Record 5320–5326, 2008

  20. Nunzio, G. De, Massafra, A., Cataldo, R., Mitri I. De, Peccarisi, M., Fantacci, M. E., Gargano, G., Torres, E. L., Approaches to juxta-pleural nodule detection in CT images within the MAGIC-5 Collaboration. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 648(1):103–106, 2011.

  21. Lee, S. L. A., Kouzani, A. Z., and Hu, E. J., Random forest based lung nodule classification aided by clustering. Comput. Med. Imaging Graph. 34(7):535–542, 2010.

    Article  Google Scholar 

  22. Armato, S. G., 3rd, Giger, M. L., and MacMahon, H., Automated detection of lung nodules in CT scans: Preliminary results. Med. Phys. 28(8):1552–1561, 2001.

    Article  Google Scholar 

  23. Gurcan, M,, Sahiner, B,, Petrick, N,, Chan, H, P., Kazerooni, E. A., Cascade, P. N., Hadjiiski, L., Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29(11):2552–2558.

  24. Suzuki, K., Armato, S. G., 3rd, Li, F., Sone, S., and Doi, K., Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med. Phys. 30(7):1602–1617, 2003.

    Article  Google Scholar 

  25. Awai, K., Murao, K., Ozawa, A., Komi, M., Hayakawa, H., Hori, S., and Nishimura, Y., Pulmonary nodules at chest CT: Effect of computer-aided diagnosis on radiologists’ detection performance. Radiology 230(2):347–352, 2004.

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Farag, A., El-Baz, A., Gimel’farb, G. G., Falk, R., Hushek, S. G. Automatic detection and recognition of lung abnormalities in helical CT images using deformable templates. Lecture Notes in Computer Science, Springer-Verlag, Medical Image Computing and Computer-Assisted Intervention 3217:856–864, 2004.

  28. Ge, Z. Y., Sahiner, B., Chan, H. P., Hadjiiski, L. M., Cascade, P. N., Bogot, N., Kazerooni, E. A., Wei, J., and Zhou, C., Computer-aided detection of lung nodules: False positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Med. Phys. 32(8):2443–2454, 2005.

    Article  Google Scholar 

  29. Brown, M. S., McNitt-Cray, M. F., Golldin, J. G., Suh, R. D., Sayre, J. W., and Aberle, D. R., Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans. Med. Imaging 20(12):1242–1250, 2001.

    Article  Google Scholar 

  30. Ye, X., Lin, X., Dehmeshki, J., Slabaugh, G., and Beddoe, G., Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans.Biomed. Eng. 56(7):1810–1820, 2009.

    Article  Google Scholar 

  31. Bağci, U., Bray, M., Caban, J., Yao, J., and Mollura, D. J., Computer-assisted detection of infectious lung diseases: A review. Comput. Med. Imaging Graph. 36(1):72–84, 2012.

    Article  Google Scholar 

  32. Choi, W. J., and Choi, T. S., Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput. Methods Prog. Biomed. 113(1):37–54, 2014.

    Article  Google Scholar 

  33. Ozekes, S., and Osman, O., Computerized lung nodule detection using 3D feature extraction and learning based algorithms. J. Med. Syst. 34(2):185–194, 2010. doi:10.1007/s10916-008-9230-0.

    Article  Google Scholar 

  34. Kuruvilla, J., and Gunavathi, K., Lung cancer classification using neural networks for CT images. Comput. Methods Prog. Biomed. 113(1):202–209, 2014.

    Article  Google Scholar 

  35. Daliri, M. R., A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. J. Med. Syst. 36(2):1001–1005, 2012. doi:10.1007/s10916-011-9806-y.

    Article  Google Scholar 

  36. Cancer Imaging Archive (2014) LIDC-IDRI. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI. Accessed 9 Oct 2014.

  37. Otsu, N., A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1):62–66, 1979.

    Article  MathSciNet  Google Scholar 

  38. Lundrgen J. Alpha Shapes http://www.mathworks.com/matlabcentral/fileexchange/28851-alpha-shapes/content/alphavol.m. Accessed 12 Feb 2014, 2010

  39. Mingqiang, Y., Kidiyo, K., Joseph, R. A survey of shape feature extraction techniques. Pattern Recognition Techniques, Technology and Applications 43–90, 2008

  40. Math Works Inc, Matlab R2011a documentation. http://www.mathworks.com/help/index.html. Accessed 12 Feb 2014, 2014

  41. Theodoridis S., Koutroumbas K. (1999) Pattern recognition. Academic Press.

  42. Liu, H., and Motoda, H., Feature selection for knowledge discovery and data mining. Kluwer Academic Publishers, Boston, 1998.

    Book  MATH  Google Scholar 

  43. Fisher, R. A., The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2):179–188, 1936. doi:10.1111/j.1469-1809.1936.tb02137.x.

    Article  Google Scholar 

  44. Cover, T. M., Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. EC-14(3):326–334, 1965. doi:10.1109/pgec.1965.264137.

    Article  Google Scholar 

  45. Cover, T. M., and Hart, P. E., Nearest neighbor pattern classification. IEEE Trans. Inf. Theory IT-13(1):21–27, 1967. doi:10.1109/TIT.1967.1053964.

    Article  Google Scholar 

  46. Rumelhart D. E., Geoffrey, E. H., Williams, R. J., Learning internal representations by error propagation. Parallel distributed processing: explorations in the microstructure of cognition 1:318–362. MIT Press, Cambridge, 1986

  47. Specht, D. F., Probabilistic neural networks. Neural Netw. 3(1):109–118, 1990. doi:10.1016/0893-6080(90)90049-Q.

    Article  Google Scholar 

  48. Vapnik, V., Estimation of dependences based on empirical data. Springer Verlag, New York, 1982.

    MATH  Google Scholar 

  49. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J., Classification and regression trees. CRC Press LLC, Florida, 1984.

    MATH  Google Scholar 

  50. Good, I. J., Probability and the weighing of evidence. Charles Griffin, London, 1950.

    MATH  Google Scholar 

  51. Dobson, A. J., An introduction to generalized linear models. Chapman & Hall, New York, 1990.

    Book  MATH  Google Scholar 

  52. Breiman, L., Bagging predictors. Mach. Learn. 24(3):123–140, 1996. doi:10.1023/A:1018054314350.

    MATH  MathSciNet  Google Scholar 

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Acknowledgments

This study has been supported by Scientific and Technological Research Council of Turkey (TÜBİTAK) 2211 National Graduate Scholarship Program.

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Affiliations

  1. Department of Computer Engineering, Ege University, Izmir, Turkey

    Erdal Taşcı & Aybars Uğur

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  1. Erdal Taşcı
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  2. Aybars Uğur
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Correspondence to Erdal Taşcı.

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This article is part of the Topical Collection on Systems-Level Quality Improvement.

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Taşcı, E., Uğur, A. Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs. J Med Syst 39, 46 (2015). https://doi.org/10.1007/s10916-015-0231-5

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  • DOI: https://doi.org/10.1007/s10916-015-0231-5

Keywords

  • Feature extraction
  • Image processing
  • Lung cancer
  • Machine learning
  • Pattern recognition