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A Segmentation Framework of Pulmonary Nodules in Lung CT Images

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An Erratum to this article was published on 04 August 2015

Abstract

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

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References

  1. Alberola-Lopez C, Martín-Fernández M, Ruiz-Alzola J. Comments on: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 2004;23(5):658–660.

    Article  PubMed  Google Scholar 

  2. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 2011;38 (2):915–931.

    Article  Google Scholar 

  3. Byrd KA, Zeng J, Chouikha M. A validation model for segmentation algorithms of digital mammography images. J Appl Sci Eng Technol 2007;1:41–50.

    Google Scholar 

  4. Chalana V, Kim Y. A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 1997;16(5):642–652.

    Article  CAS  PubMed  Google Scholar 

  5. Dehmeshki J, Amin H, Valdivieso M, Ye X. Segmentation of pulmonary nodules in thoracic CT scans: A region growing approach. IEEE Trans Med Imaging 2008;27(4):467–480.

    Article  CAS  PubMed  Google Scholar 

  6. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans Biomed Eng 2011;58(12):3418–3428.

    Article  PubMed  Google Scholar 

  7. Diciotti S, Picozzi G, Falchini M, Mascalchi M, Villari N, Valli G. 3-D segmentation algorithm of small lung nodules in spiral CT images. IEEE Trans Inf Technol Biomed 2008;12(1):7–19.

    Article  CAS  PubMed  Google Scholar 

  8. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, 0lli S. Miettinen: CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules. Am J Roentgenol 2002; 178(5):1053–1057.

  9. Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993;15(9):850–863.

    Article  Google Scholar 

  10. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. Am J Roentgenol 2000;175(5):1329–1334.

    Article  CAS  Google Scholar 

  11. Ko JP, Naidich DP. Computer-aided diagnosis and the evaluation of lung disease. J Thorac Imaging 2004; 19(3):136–155.

    Article  PubMed  Google Scholar 

  12. Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images. IEEE Trans Med Imaging 2003;22(10):1259–1274.

    Article  PubMed  Google Scholar 

  13. Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A. Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 2011;15(1):133–154.

    Article  PubMed  Google Scholar 

  14. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen HO. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 2006;25(4):417–434.

    Article  PubMed  Google Scholar 

  15. Li Z, Ma L, Jin X, Zheng Z. A new feature-preserving mesh-smoothing algorithm. Vis Comput 2009; 25(2):139–148.

    Article  Google Scholar 

  16. McNitt-Gray MF, Armato III SG, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing PYD, Yankelevitz DF, Aberle DR, Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP. The lung image database consortium LIDC data collection process for nodule detection and annotation. Acad Radiol 2007;14(12):1464– 1474.

    Article  PubMed Central  PubMed  Google Scholar 

  17. Moltz JH, Kuhnigk JM, Bornemann L, Peitgen H. Segmentation of juxtapleural lung nodules in ct scan based on ellipsoid approximation. First International Workshop on Pulmonary Image Processing, 2008.

  18. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989;12(7):629–639.

    Article  Google Scholar 

  19. Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ. On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 2006;25(4):435–450.

    Article  PubMed  Google Scholar 

  20. Santos BS, Ferreira C, Silva JS, Silva A, Teixeira L. Quantitative evaluation of a pulmonary contour segmentation algorithm in x-ray computed tomography images 1. Acad Radiol 2004;11(8):868–878.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  22. Silva A, Silva JS, Santos BS, Ferreira C. Fast pulmonary contour extraction in x-ray CT images: a methodology and quality assessment. Medical Imaging 2001, 2001, pp 216–224.

  23. Silva JS, Santos JB, Roxo D, Martins P, Castela E, Martins R. Algorithm versus physicians variability evaluation in the cardiac chambers extraction. IEEE Trans Inf Technol Biomed 2012;16(5):835–841.

    Article  PubMed  Google Scholar 

  24. Tao Y, Lu L, Dewan M, Chen AY, Corso J, Xuan J, Salganicoff M, Krishnan A. Multi-level ground glass nodule detection and segmentation in ct lung images. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009, pp 715–723. Springer; 2009.

  25. Zhou J, Chang S, Metaxas DN, Zhao B, Ginsberg MS, Schwartz LH. An automatic method for ground glass opacity nodule detection and segmentation from CT studies. Engineering in Medicine and Biology Society, EMBS’06, pp 3062–3065; 2006.

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Acknowledgments

This study was funded by Department of Electronics and Information Technology, Government of India, Grant number 1(3)2009-ME &TMD and 1(2)/2013-ME & TMD/ESDA, respectively.

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Correspondence to Sudipta Mukhopadhyay.

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Mukhopadhyay, S. A Segmentation Framework of Pulmonary Nodules in Lung CT Images. J Digit Imaging 29, 86–103 (2016). https://doi.org/10.1007/s10278-015-9801-9

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  • DOI: https://doi.org/10.1007/s10278-015-9801-9

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