Journal of Medical Systems

, Volume 34, Issue 4, pp 419–433 | Cite as

A Segmentation Method of Lung Cavities Using Region Aided Geometric Snakes

  • Alireza Osareh
  • Bita Shadgar
Original Paper


Segmenting the lungs in medical images is a challenging and important task for many applications. In particular, automatic segmentation of lung cavities from multiple magnetic resonance (MR) images is very useful for oncological applications such as radiotherapy treatment planning. Largely changing lung shapes, low contrast and poorly defined boundaries make the lung cavities hard to be distinguished, even in the absence of prominent neighboring structures. In this paper, we utilized a modified geometric-based snake model which could greatly improve the model’s segmentation efficiency in capturing complex geometries and dealing with difficult initialization and weak edges. This model integrates the gradient flow forces with region constraints provided by fuzzy c-means clustering. The proposed model has been tested on a database of 30 MR images with 80 slices in each image. The obtained results are compared to manual segmentations of the lung provided by an expert radiologist and with those of previous works, showing encouraging results and high robustness of our approach.


Image segmentation Radiotherapy planning Magnetic resonance images Snakes Lung cavities 


  1. 1.
    Early Breast Cancer Trialists’ Collaborative Group., Radiotherapy for early breast cancer. Cochrane Database of Systematic Reviews 2002, Issue 2.Google Scholar
  2. 2.
    Huber, P., Jenne, J., Rastert, R., and Simiantonakis, I., A new noninvasive approach in breast cancer therapy using magnetic resonance imaging-guided focused ultrasound surgery. Cancer Res. 61:8441–8447, 2001.Google Scholar
  3. 3.
    Evans, P., Donovan, E., and Partridge, M., The delivery of intensity modulated radiotherapy to the breast using multiple static fields. Radiother. Oncol. 57:79–89, 2000. doi: 10.1016/S0167-8140(00)00263-2.CrossRefGoogle Scholar
  4. 4.
    Cho, B., Hurkmans, C., Damen, E., and Zijp, L., Intensity modulated versus non-intensity modulated radiotherapy in the treatment of left breast and upper internal mammary lympth node chauin: a comparative planning study. Radiother. Oncol. 62:127–136, 2002. doi: 10.1016/S0167-8140(01)00472-8.CrossRefGoogle Scholar
  5. 5.
    Kinhikar, R., Deshpande, S., Mahantshetty, U., and Sarin, R., HDR brachytherapy combined with 3D conformal versus IMRT in left-sided breast cancer patients including internal mammary chain: comparative analysis of dosimetric and technical parameters. J. Appl. Clin. Med. Phys. 6:1–12, 2005. doi: 10.1120/jacmp.2025.25344.CrossRefGoogle Scholar
  6. 6.
    Hu, S., Hoffman, E., and Reinhardt, J., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging. 20:490–498, 2001. doi: 10.1109/42.929615.CrossRefGoogle Scholar
  7. 7.
    Middleton, I., and Damper, R., Segmentation of magnetic resonance images using a combination of neural networks and active contour models. Med. Eng. Phys. 26:71–86, 2004. doi: 10.1016/S1350-4533(03)00137-1.CrossRefGoogle Scholar
  8. 8.
    Itai, Y., Kim, H., and Ishikawa, S., A segmentation method of lung areas by using snakes and automatic detection of abnormal shadow on the areas. International Journal of Innovative Computing. Inf. Contr. 3:277–284, 2007.Google Scholar
  9. 9.
    Silveria, M., Marques, J., Automatic segmentation of the lungs using multiple active contours and outlier model, in: International Conference of the IEEE Engineering in Medicine and Biology, (pp. 3122–3125), 2006.Google Scholar
  10. 10.
    Brown, M., McNitt-Gray, M., and Mankovich, N., Method for segmenting chest CT image data using an anatomical model: preliminary results. IEEE Trans. Med. Imaging. 16:828–839, 1997. doi: 10.1109/42.650879.CrossRefGoogle Scholar
  11. 11.
    Clarke, L., Velthuizen, R., Camacho, M., and Heine, J., MRI segmentation: methods and applications. Magn. Reson. Imaging. 13:343–368, 1995. doi: 10.1016/0730-725X(94)00124-L.CrossRefGoogle Scholar
  12. 12.
    Ray, N., Acton, S., Altes, T., Lange, E., and Brookeman, J., Merging parametric active contours within homogeneous image regions for MRI-Based lung segmentation. IEEE Trans. Med. Imaging. 22:189–199, 2003. doi: 10.1109/TMI.2002.808354.CrossRefGoogle Scholar
  13. 13.
    Caselles, V., Catte, F., Coll, T., and Dibos, F., A geometric model for active contours. Numerische Math. 66:1–31, 1993. doi: 10.1007/BF01385685.MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis. 1:321–331, 1988. doi: 10.1007/BF00133570.CrossRefGoogle Scholar
  15. 15.
    Xie, X., and Mirmehdi, M., RAGS: Region-aided geometric snake. IEEE Trans. Image Process. 13:640–652, 2004. doi: 10.1109/TIP.2004.826124.CrossRefMathSciNetGoogle Scholar
  16. 16.
    Comaniciu, D., and Meer, P., Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24:603–619, 2002. doi: 10.1109/34.1000236.CrossRefGoogle Scholar
  17. 17.
    Webb, S., The Physics of Medical Imaging. Adam Hilger, Bristol, UK, 1988.Google Scholar
  18. 18.
    Malladi, R., Sethian, J., and Vemuri, B., Evolutionary fronts for topology independent shape modeling and recovery, in European Conference on Computer Vision (pp. 3–13). Stockholm, Sweden, 1994Google Scholar
  19. 19.
    Caselles, V., Kimmel, R., and Sapiro, G., Geodesic active contour. Int. J. Comput. Vis. 22:61–79, 1997. doi: 10.1023/A:1007979827043.MATHCrossRefGoogle Scholar
  20. 20.
    Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A., Gradient flows and geometric active contour models, in International Conference on Computer Vision, (pp. 810–815), Boston, USA, 1995.Google Scholar
  21. 21.
    Sapiro, G., Color snakes. Comput. Vis. Image Underst. 68:247–253, 1997. doi: 10.1006/cviu.1997.0562.CrossRefGoogle Scholar
  22. 22.
    Siddiqi, K., Lauziere, Y., Tannenbaum, A., and Zucker, S., Area and length minimizing flows for shape segmentation. IEEE Trans. Image Process. 7:433–443, 1998. doi: 10.1109/83.661193.CrossRefGoogle Scholar
  23. 23.
    Xu, C., and Prince, J., Generalized gradient vector flow external forces for active contours. Signal Processing. 71:131–139, 1998. doi: 10.1016/S0165-1684(98)00140-6.MATHCrossRefGoogle Scholar
  24. 24.
    Deng, Y., and Manjunath, B., Unsupervised segmentation of color-texture regions in images. IEEE Trans. Pattern Anal. Mach. Intell. 23:800–810, 2001. doi: 10.1109/34.946985.CrossRefGoogle Scholar
  25. 25.
    Pham, D., and Prince, J., Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imaging. 18:737–752, 1999. doi: 10.1109/42.802752.CrossRefGoogle Scholar
  26. 26.
    Xu, C., and Prince, J., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7:359–369, 1998. doi: 10.1109/83.661186.MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Bezdek, J., Keller, J., Krisnapuram, R., and Pal, N., Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic, Boston, 1999.MATHGoogle Scholar
  28. 28.
    Pham, D., Prince, J., Dagher, A., and Xu, C., An automated technique for statistical characterization of brain tissues in magnetic resonance imaging. Int. J. Pattern Recognit. Artif. Intell. 11:1189–1211, 1997. doi: 10.1142/S021800149700055X.CrossRefGoogle Scholar
  29. 29.
    Hall, L., Bensaid, A., Clarke, L., Velthuizen, P., Silbiger, M., and Bezdek, J., A comparison of neural networks and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans. Neural Netw. 3:672–682, 1992. doi: 10.1109/72.159057.CrossRefGoogle Scholar
  30. 30.
    Bezdek, J., A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2:1–8, 1980.MATHCrossRefGoogle Scholar
  31. 31.
    Sonka, M., Hlavac, V., and Boyle, R., Image Processing, Analysis, and Machine Vision, PWS Publishing, (1999).Google Scholar
  32. 32.
    Pratt, W., Digital Image Processing. Wiley, New York, 1991.MATHGoogle Scholar
  33. 33.
    Rijsbergen, V., Information retrieval. Butterworth, London, 1979.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Computer Science Department, Engineering FacultyShahid Chamran UniversityAhvazIran

Personalised recommendations