Skip to main content

Advertisement

SpringerLink
Log in

A Segmentation Method of Lung Cavities Using Region Aided Geometric Snakes

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Early Breast Cancer Trialists’ Collaborative Group., Radiotherapy for early breast cancer. Cochrane Database of Systematic Reviews 2002, Issue 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  14. Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis. 1:321–331, 1988. doi:10.1007/BF00133570.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  17. Webb, S., The Physics of Medical Imaging. Adam Hilger, Bristol, UK, 1988.

    Google Scholar 

  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, 1994

  19. Caselles, V., Kimmel, R., and Sapiro, G., Geodesic active contour. Int. J. Comput. Vis. 22:61–79, 1997. doi:10.1023/A:1007979827043.

    Article  MATH  Google Scholar 

  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.

  21. Sapiro, G., Color snakes. Comput. Vis. Image Underst. 68:247–253, 1997. doi:10.1006/cviu.1997.0562.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  27. Bezdek, J., Keller, J., Krisnapuram, R., and Pal, N., Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic, Boston, 1999.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  30. Bezdek, J., A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2:1–8, 1980.

    Article  MATH  Google Scholar 

  31. Sonka, M., Hlavac, V., and Boyle, R., Image Processing, Analysis, and Machine Vision, PWS Publishing, (1999).

  32. Pratt, W., Digital Image Processing. Wiley, New York, 1991.

    MATH  Google Scholar 

  33. Rijsbergen, V., Information retrieval. Butterworth, London, 1979.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Engineering Faculty, Shahid Chamran University, Ahvaz, Iran

    Alireza Osareh & Bita Shadgar

Authors
  1. Alireza Osareh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bita Shadgar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Osareh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Osareh, A., Shadgar, B. A Segmentation Method of Lung Cavities Using Region Aided Geometric Snakes. J Med Syst 34, 419–433 (2010). https://doi.org/10.1007/s10916-009-9255-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9255-z

Keywords

Search

Navigation