Journal of Bioinformatics and Biological Engineering   
Journal of Bioinformatics and Biological Engineering(JBBE)
Frequency: Annually
An Innovative Soft Computing Framework to Measure and Classify Solid Pulmonary Tumors from CT Images
Full Paper(PDF, 1486KB)
Assessing the shrinking of lung nodules in response to the treatment is an important task to evaluate the effectiveness of the therapy, so guidelines to minimize variability and make this task objective and observer-independent are strongly needed. The Response Evaluation Criteria in Solid Tumors (RECIST) is a set of standardized rules that defines when cancer patients improve, stay the same, or deteriorate during the treatments. An accurate evaluation of the growth rate can be performed only by means of a high resolute segmentation, followed by volumetric techniques, although the higher the resolution, the more time is consumed in the process. In this work we present a framework that carries out a fully automatic 3D segmentation of the human respiratory system in Computer Tomography images. Usually, an analysis of the lungs requires manual segmentation of nodules that is always subjective and often error-prone. So the framework encompasses a second step, which semi-automatically segments the lung nodules, extracting them from the surrounding tissues and providing an accurate data set for estimating several volumetric features. These features can be used for the classification of the malignancy of the nodule.
Keywords:3D segmentation; CT Images; Lung Nodules; Region Growing; Level Set; Metaball; GENOCOP; Neural Network
Author: Vitoantonio Bevilacqua1, Daniele Ranieri2, Gaetano Nacci1, Gioacchino Brunetti2, Piero Larizza2, Francescomaria Marino3
1.DEI–Dipartimento di Ingegneria Elettrica e dell’Informazione–Politecnico di Bari, Via Orabona, 4–70125 Bari, Italy
2.MASMEC Biomed–Divisione di MASMEC S.p.A., Via delle Violette, 14–70026 Modugno (BA), Italy
3.APIS–Apulia Intelligent Systems, Via Pasquale Fiore, 26–70125 Bari, Italy
  1. World Health Organization (1979), WHO Handbook for reporting results of cancer treatment, WHO Publication, no. 48, Geneva, 1979.
  2. A. B. Miller, B. Hoogstraten, M. Staquet, and A. Winkler, “Reporting results of cancer treatment,” Cancer, vol. 47, pp. 207-214, 1981.
  3. P. Therasse, S. G. Arbuck, E. A. Eisenhauer, J. Wanders, R. S. Kaplan, L. Rubinstein, J. Verweij, M. Van Glabbeke, A. T. Van Oosterom, M. C. Christian, S. G. Gwyther, “New guidelines to evaluate the response to treatment in solid tumors,” J Natl Cancer Inst., vol. 92, pp. 205-216, 2000.
  4. E. A. Eisenhauer, P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent, R. and et al., “New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1),” Eur J Cancer, vol. 45, pp. 228-247, 2009.
  5. M. Revel, C. Lefort, A. Bissery, M. Bienvenu, L. Aycard, G. Chatellier, and G. Frija, “Pulmonary nodules: Preliminary experience with threedimensional evaluation,” Radiology, vol. 231, pp. 459-466, 2004.
  6. L. Goodman, M. Gulsun, L. Washington, P. Nagy, and K. Piacsek, “Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measuements,” Amer. J. Roentgenol., vol. 186, pp. 989-994, 2006.
  7. K. Marten, F. Auer, S. Schmidt, and et al. “Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria,” Eur. Radiol., vol. 16, pp. 781-790, 2006.
  8. Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,” IEEE Trans. Med. Imag., vol. 20, no. 7, pp. 595-604, Jul. 2001.
  9. B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang, and H. Schwartz, “Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm,” J. Appl. Clin. Med. Phys., vol. 4, no. 3, pp. 248-260, 2003.
  10. K. Suzuki, S. G. Armota, F. Li, S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT,” Med. Phys., vol. 30, no. 7, pp. 1602-1617, Jul. 2003.
  11. Z. Y. Ge, B. Sahiner, H. P. Chan, L. M. Hadjiiski, P. N. Cascade, N. Bogot, E. A. Kazerooni, J.Wei, and C. Zou, “Computer-aided detection of lung nodules: False positive reduction using a 3-D gradient field method and 3-D ellipsoid fitting,” Med. Phys., vol. 32, pp. 2443-2454, 2005.
  12. M. S. Brown, M. F. McNitt-Gray, J. G. Goldin, R. D. Suh, J. W. Sayre, and D. R. Aberle, “Patient-specific models for lung nodule detection and surveillancein CT images,” IEEE Trans. Med. Imag., no. 12, pp. 1242-1250, Dec. 2001.
  13. J. P. Ko, H. Rusinek, E. L. Jacobs, J. S. Babb, M. Betke, G. McGuinness, and D. P. Naidich, “Small pulmonary nodules: Volume measurement at chest CT-phantom study,” Radiology, vol. 228, no. 8, pp. 864-780, 2003.
  14. W. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “3-D segmentation and growth-rate estimation of small pulmonary nodules in helical CT images,” IEEE Trans. Med. Imag., vol. 22, no. 10, pp. 1259-1274, 2003.
  15. B. Zhao, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “Threedimensionalmulticriterion automatic segmentation of pulmonary nodules of helical computed tomography images,” Opt. Eng., vol. 38, no. 8, pp. 1340-1347, 1999.
  16. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “Threedimensional curvature analysis of small pulmonary nodules in helical CT scans,” Suppl. Radiol., vol. 217P, p. 549, 2000, RSNA Scientific Program 2000.
  17. J. Goo, T. Tongdee, R. Tongdee, K. Yeo, C. Hildebolt, and K. Bae, “Volumetric measurement of synthetic lung nodules with multi-detector row CT: Effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy,” Radiology, vol. 235, pp. 850-856, 2005.
  18. S. Diciotti, S. Lombardo, M. Falchini, G. Picozzi, and M. Mascalchi, “Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis”, IEEE Transactions on Biomedical Engineering, vol. 58, no. 12, Part: 1, pp. 3418-3428, 2011.
  19. S. Diciotti, G. Picozzi, M. Falchini, M. Mascalchi, N. Villari, and G. Valli, “3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Image” IEEE Transactions on Information Technology in Biomedicine, vol. 12, M. 1, pp. 7-19, 2008.
  20. J. M. Kuhnigk, V. Dicken, L. Bornemann, and et al. “Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans,” IEEE Trans. Med. Imag., vol. 25, no. 4, pp. 417–434, 2006.
  21. A. P. Reeves, A. B. Chan, D. F. Yankelevitz, C. I. Henschke, B. Kressler, andW. J.Kostis, “On measuring the change in size of pulmonary nodules,” IEEE Trans. Med. Imag., vol. 25, no. 4, pp. 435-450, 2006.
  22. W. Mullally, M. Betke, J. Wang, and J. P. Ko, “Segmentation of nodules on chest computed tomography for growth assessment,” Med. Phys., vol. 31, no. 4, pp. 839-848, 2004.
  23. S. Shanhui, C. Bauer, and R. Beichel, “Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach”, IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 449-460, 2012.
  24. T. Kitasaka, K. Mori, J. Hasegawa, and J. Toriwaki, “Lung area extraction from 3-D chest X-ray CT images using a shape model generated by a variable Bézier surface,” Syst. Comput. Jpn., vol. 34, no. 4, pp. 60-71, 2003.
  25. J. Wang, Q. Li, and F. Li, “Automated segmentation of lungs with severe interstitial lung disease in CT,” Med. Phys., vol. 36, no. 10, pp. 4592-4599, 2009.
  26. J. H. Moltz, L. Bornemann, J. M. Kuhnigk, V. Dicken, E. Peitgen, S. Meier, and et al “Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged LymphNodes in CT Scans”, IEEE Journal of Selected Topic in Signal Processing, vol. 3, no. 1, pp. 122-134, 2009.
  27. K. Okada, V. Ramesh, A. Krishnan, M. Singh, and U. Akdemir, “Robust pulmonary nodule segmentation in CT: Improving performance for juxtapleural cases,” in Proc. MICCAI, pp. 781-789, 2005.
  28. C. Fetita, F. Preteaux, C. Beigelman-Aubry, and P. Grenier, “3D automatic lung nodule segmentation in HRCT,” in Proc. Medical Image Computing and Computer-Assisted Intervention MICCAI, (Series Lecture Notes in Computer Science 2878) R. E. Ellis and T. M. Peters, Eds. Berlin, Germany: Springer-Verlag, pp. 626-634, 2003.
  29. J. Pu, D. S. Paik, X. Meng, J. E. Roos, and G. D. Rubin, “Shape breakand-repair strategy and its application to automated medical image segmentation,” IEEE Trans. Vis. Comput. Graphics, vol. 17, no. 1, pp. 115-124, Jan. 2011.
  30. M. N. Prasad, M. S. Brown, S. Ahmad, F. Abtin, J. Allen, I. da Costa, H. J. Kim, M. F. McNitt-Gray, and J. G. Goldin, “Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs,” Acad. Radiol., vol. 15, no. 9, pp. 1173-1180, 2008.
  31. Y. Kawata, N. Niki, H. Oshmatsu, K. Eguchi, and N. Moriyama, “Shape analysis of pulmonary nodules based on thin section CT images,” in Proc. SPIE, vol. 3034, pp. 964-974, 1997.
  32. H. Shen, B. Goebel, and B. Odry, “A new algorithm for local surface smoothing with application to chest wall nodule segmentation in lung CT data,” in Proc. SPIE, 2004, vol. 5370, pp. 1519–1526.
  33. L. Fan, J. Qian, B. L. Odry, and H. Shen, “Automatic segmentation of pulmonary nodules by using dynamic 3-D cross-correlation for interactive CAD systems,” in Proc. SPIE Med. Imag., vol. 4684, pp. 1362-1369, 2002.
  34. K. Okada, D. Comaniciu, and A. Krishnan, “Robust anisotropic gaussian fitting for columetric characterization of pulmonary nodules in multislicect,” IEEE Trans. Med. Imag., vol. 24, no. 3, pp. 409-423, Mar. 2005.
  35. K. Okada, U. Akdemir, and A. Krishnan, “Blob segmentation using joint space-intensity likelihood ratio test: Application to 3-D tumor segmentation,” in IEEE Conf. Comput. Vis. Pattern Recognit., no. 2, pp. 437-444, Jun. 2005,.
  36. J. K. Udupa and S. Samarasekera, “Fuzzy connectedness and object delineation: Theory, algorithm, and validation,” Graph. Models Image Process., vol. 58, no. 3, pp. 246-261, 1996.
  37. S. A. Hijjatoleslami and J. Kitter, “Region growing: A new approach,” IEEE Trans. Image Process., vol. 7, no. 7, pp. 1079-1084, Jul. 1998.
  38. J. Dehmeshki, H. Amin, M. Valdivieso, and X. Ye, “Segmentation of pulmonary nodules in thoracic CT scans: A region growing approach,” IEEE Trans. Med. Imag., vol. 27, no. 4, pp. 467-480, 2008.
  39. T. Kubota, A. K. Jerebko, M. Dewan, M. Salganicoff, and A. Krishnan, “Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models,” Med. Image Anal., vol. 15, no. 1, pp. 133-154, 2011.
  40. R. Wiemker, P. Rogalla, T. Blaffert, D. Sifri, O. Hay, E. Shah, R. Truyen, and T. Fleiter, “Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT,” Brit. J. Radiol., vol. 78, pp. S46-S56, 2005.
  41. B. van Ginneken, “Supervised probabilistic segmentation of pulmonary nodules in CT scans,” in Proc. MICCAI, pp. 912-919, 2006.
  42. R. C. Hardie, S. K. Rogers, T. Wilson, and A. Rogers, “Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs,” Med. Image Anal., vol. 12, pp. 240-258, 2008.
  43. Q. Li, F. Li, and K. Doi, “Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier,” Acad. Radiol., vol. 15, no. 2, pp. 165-175, 2008.
  44. Bellotti R et al, “Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region,” Journal of digital imaging, 2009.
  45. J. Heuberger et al., “Lung CT segmentation for image retrieval using the Insight Toolkit,” Medical Imaging and Telemedicine, 2005.
  46. S. Hu, E. A. Hoffman, and J. M. M. Reinhardt, “Automatic lung segmentation for accurate quantitation of volumetric X–ray CT images,” IEEE Transactions on Medical Imaging, vol. 20, no. 6, pp. 490-498,2001.
  47. G. De Nunzio et al., “Lung Parenchima Segmentation in CT Scans as a Preprocessing Step for Automatic Nodule Detection,” TCN CAE 2005, International Conference on CAE and Computational Technologies for Industry, Lecce (Italy), 2005.
  48. M. S. Brown, M. F. McNitt-Gray, N. J. Mankovich, J. G. Goldin, J. Hiller, L. S. Wilson, D. R. Aberle, “Method for segmenting chest CT image data using an anatomical model: preliminary result,” IEEE Trans Med Imag, vol. 16, no. 6, pp. 828-839, 1997.
  49. R. Shojaii, J. Alirezaie, and P Babyn, “Automatic lung segmentation in CT Images using watershed transform,” IEEE International Conference on Image Processing, Genoa (Italy), 2005
  50. S. G. Armato and W. F. Sensakovic, “Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis,” Acad. Radiol, vol. 11, pp. 1011-1021, 2004
  51. A. C. Silva, P. C. P. Carvalho, and R. A. Nunes, “Segmentation and reconstruction of the pulmonary parenchyma,” Vision and Graphics Laboratory, Institute of Pure and Applied Mathematics, Rio de Janeiro, Tech. Rep., 2002.
  52. G. J. Kemerink, R. J. S. Lamers, B. J. Pellis, K. H. H., and J. M. A.vanEngelshoven, “On segmentation of lung parenchyma in quantitative computed tomography of the lung,” Medical Physics, vol. 25, no. 12,pp. 2432-2439, 1998.
  53. J. K. Leader, B. Zheng, R. M. Rogers et al. “Automated lung segmentation in X–ray computed tomography: Development and evaluation of a heuristic threshold–based scheme,” Academic Radiology, vol. 10, pp. 1224-1236,(2003).
  54. A. El-Baz, A. A. Farag, R. Falk, and R. La Rocca, “Detection, visualization, and identification of lung abnormalities in chest spiral CT scans: Phase I,” in Proc. of the International Conf. on Biomedical Engineering, Cairo, Egypt, 2002.
  55. R. Bellotti et al., “A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model,” Med Phys, vol. 34, no. 12, pp. 4901-4910, 2007.
  56. J. A. Sethian, “Level Set Methods and Fast Marching Methods,” Cambridge Press, Second edition, 1999.
  57. K. Jan-Martin et al, Lung lobe segmentation by anatomy-guided 3D watershed transform, 2003.
  58. D. Aykac, E. A. Hoffman, G. McLennan, and J. M. Reinhardt, “Segmentation and analysis of the human airway tree from threedimensional X-ray CT images,” IEEE Trans Medical Imaging, vol. 22, pp. 8, pp. 940-950, 2003.
  59. V. Bevilacqua, et al. “3D measurements for tumors malignancies early diagnosis,” Medical Measurements and Applications Proceedings (MeMeA), 2011 IEEE International Workshop on, Bari, 2011.
  60. W. Lorensen and H. Cline, “Marching cubes: a high resolution 3D surface construction algorithm,” Computer Graphics, vol. 21, no. 4, pp. 163-169, 1987.
  61. S. P. Lloyd., “Least squares quantization in PCM,” IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129-137, 1982.
  62. J. Blinn, “A Generalization of Algebraic Surface Drawing,” ACM Transaction on Graphics, vol. 1, no. 3, pp. 235-256, 1982.
  63. Z. Michalewicz and G. Nazhiyath, “Genocop III: A Co-evolutionary Algorithm for Numerical Optimization Problems with Nonlinear Constraints, Evolutionary Computation, 1995,” IEEE International Conference on Evolutionary Computation, vol. 2, pp. 647-651, 1996.
  64. V. Bevilacqua, G. Filograno, M. Fiorentino, and A. E. Uva, “Early diagnosis of lung tumors by genetically optimized 3D-metaball malignancy metric,” Proccedings of GECCO Companion '12 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, pp. 531-538, 2012.
  65. W. Chunliang, H. Frimmel, and ÖrjanSmedby, “Level-set based vessel segmentation accelerated with periodic monotonic speed function,” SPIE medical imaging 2011 Lake Buena Vista, Florida, USA, 2011.
  66. J. A. Sethian, “A fast marching level set method for monotonically advancing fronts,” Proc. Natl. Acad. Sci., USA, vol. 93, pp. 1591-1595, 1996.
  67. Cloud Compare tool. [online]. Available:
  68. A. Zhao, R. Ying, and et al. “NELSON lung cancer screening study,” Cancer Imaging, vol. 11, S79 S84, 2011. [online]. Available:
  69. V. Bevilacqua, “Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: New tests on an enlarged cohort of polyps,” NEUROCOMPUTING, 2012.
  70. V. Bevilacqua, G. Mastronardi, and G. Piscopo, “Evolutionary Approach to Inverse Planning in Coplanar Radiotherapy,” Image And Vision Computing, vol. 25, no. 2, pp. 196-203, 2006.