Medical & Biological Engineering & Computing

, Volume 51, Issue 10, pp 1091–1104 | Cite as

Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification

  • Hung-Ting Liu
  • Tony W. H. Sheu
  • Herng-Hua Chang
Original Article

Abstract

Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.

Keywords

Skull-stripping Segmentation Active contours Fuzzy possibilistic c-means MRI 

References

  1. 1.
    Barni M, Cappellini V, Mecocci A (1996) Comments on a possibilistic approach to clustering. Fuzzy Syst IEEE Trans 4(3):393–396. doi:10.1109/91.531780 CrossRefGoogle Scholar
  2. 2.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Norwell, MA, USACrossRefGoogle Scholar
  3. 3.
    Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lders E, Rottenberg D (2004) Quantitative comparison of four brain extraction algorithms. NeuroImage 22(3):1255–1261PubMedCrossRefGoogle Scholar
  4. 4.
    Chang HH, Zhuang AH, Valentino DJ, Chu WC (2009) Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47(1):122–135. doi:10.1016/j.neuroimage.2009.03.068 PubMedCrossRefGoogle Scholar
  5. 5.
    Charfi M (2010) Using the ggvf for automatic initialization and splitting snake model. In: I/V communications and mobile network (ISVC), 5th international symposium on IEEE. pp 1–4. doi:10.1109/ISVC.2010.5656426
  6. 6.
    Chiverton J, Wells K, Lewis E, Chen C, Podda B, Johnson D (2007) Statistical morphological skull stripping of adult and infant mri data. Comput Biol Med 37(3):342–357. doi:10.1016/j.compbiomed.2006.04.001 PubMedCrossRefGoogle Scholar
  7. 7.
    Cohen L, Cohen I (1993) Finite-element methods for active contour models and balloons for 2-d and 3-d images. Pattern Anal Mach Intell IEEE Trans 15(11):1131–1147. doi:10.1109/34.244675 CrossRefGoogle Scholar
  8. 8.
    Cohen LD (1991) On active contour models and balloons. CVGIP Image Underst 53:211–218. doi:10.1016/1049-9660(91)90028-N CrossRefGoogle Scholar
  9. 9.
    Ellis CA, Parbery SA (2005) Is smarter better? a comparison of adaptive, and simple moving average trading strategies. Res Int Bus Financ 19(3):399–411. doi:10.1016/j.ribaf.2004.12.009 CrossRefGoogle Scholar
  10. 10.
    Fennema-Notestine C, Ozyurt IB, Clark CP, Morris S, Bischoff-Grethe A, Bondi MW, Jernigan TL, Fischl B, Segonne F, Shattuck DW, Leahy RM, Rex DE, Toga AW, Zou KH, Brown GG (2006) Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Hum Brain Mapp 27(2):99–113PubMedCrossRefGoogle Scholar
  11. 11.
    Fenster A, Chiu B (2005) Evaluation of segmentation algorithms for medical imaging. In: Engineering in Medicine and Biology Society. IEEE-EMBS 2005. 27th annual international conference. pp 7186–7189. doi:10.1109/IEMBS.2005.1616166
  12. 12.
    IDeA (2013) IDeA lab: imaging of dementia and aging, Center for Neuroscience, UC Davis. http://idealab.ucdavis.edu/
  13. 13.
    Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recognit 46(3):914–924CrossRefGoogle Scholar
  14. 14.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefGoogle Scholar
  15. 15.
    Krishnapuram R, Keller J (1993) A possibilistic approach to clustering. Fuzzy Syst IEEE Trans 1(2):98–110. doi:10.1109/91.227387 CrossRefGoogle Scholar
  16. 16.
    Li CM, Liu JD, Fox M (2005) Segmentation of edge preserving gradient vector flow: an approach toward automatically initializing and splitting of snakes. In: Computer vision and pattern recognition. CVPR 2005. IEEE computer society conference, vol 1. pp 162–167. doi:10.1109/CVPR.2005.314
  17. 17.
    Li H, Yezzi A, Cohen LD (2006) 3d brain segmentation using dual-front active contours with optional user interaction. Int J Biomed Imaging (Article ID 53186):17. doi:10.1155/IJBI/2006/53186
  18. 18.
    McGill (2011) BrainWeb: simulated brain database. http://www.bic.mni.mcgill.ca/brainweb/
  19. 19.
    MGH (2003) Internet brain segmentation repository (IBSR). http://www.cma.mgh.harvard.edu/ibsr/, Accessed 06 Dec 2003
  20. 20.
    Pal N, Pal K, Bezdek J (1997) A mixed c-means clustering model. In: Fuzzy systems. Proceedings of the sixth IEEE international conference, vol 1. pp 11–21. doi:10.1109/FUZZY.1997.616338
  21. 21.
    Pal N, Pal K, Keller J, Bezdek J (2005) A possibilistic fuzzy c-means clustering algorithm. Fuzzy Syst IEEE Trans 13(4):517–530. doi:10.1109/TFUZZ.2004.840099 CrossRefGoogle Scholar
  22. 22.
    Park JG, Lee C (2009) Skull stripping based on region growing for magnetic resonance brain images. NeuroImage 47(4):1394–1407. doi:10.1016/j.neuroimage.2009.04.047 PubMedCrossRefGoogle Scholar
  23. 23.
    Phumeechanya S, Pluempitiwiriyawej C, Thongvigitmanee S (2010) Edge type-selectable active contour using local regional information on extendable search lines. In: Image processing (ICIP), 17th IEEE international conference. pp 653–656. doi:10.1109/ICIP.2010.5650160
  24. 24.
    Pitiot A, Delingette H, Thompson PM, Ayache N (2004) Expert knowledge-guided segmentation system for brain {MRI}. NeuroImage 23(Suppl 1):S85–S96. doi:10.1016/j.neuroimage.2004.07.040
  25. 25.
    Rkkumar (2011) Snakes—Active contour models: demonstrates the use active contour models. http://www.seas.harvard.edu/~rkkumar. Accessed 01 Aug 2011
  26. 26.
    Sgonne F, Dale A, Busa E, Glessner M, Salat D, Hahn H, Fischl B (2004) A hybrid approach to the skull stripping problem in MRI. NeuroImage 22(3):1060–1075. doi:10.1016/j.neuroimage.2004.03.032 CrossRefGoogle Scholar
  27. 27.
    Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM (2001) Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5):856–876. doi:10.1006/nimg.2000.0730 PubMedCrossRefGoogle Scholar
  28. 28.
    Shawe-Taylor J, Sun S (2011) A review of optimization methodologies in support vector machines. Neurocomputing 74(17):3609–3618. doi:10.1016/j.neucom.2011.06.026 CrossRefGoogle Scholar
  29. 29.
    Shi F, Wang L, Dai Y, Gilmore JH, Lin W, Shen D (2012) Label: pediatric brain extraction using learning-based meta-algorithm. NeuroImage 62(3):1975–1986. doi:10.1016/j.neuroimage.2012.05.042 PubMedCrossRefGoogle Scholar
  30. 30.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155. doi:10.1002/hbm.10062 PubMedCrossRefGoogle Scholar
  31. 31.
    Stokking R, Vincken KL, Viergever MA (2000) Automatic morphology-based brain segmentation (mbrase) from mri-t1 data. NeuroImage 12(6):726–738. doi:10.1006/nimg.2000.0661 PubMedCrossRefGoogle Scholar
  32. 32.
    Sun S, Zhang C (2006) Adaptive feature extraction for eeg signal classification. Med Biol Eng Comput 44(10):931–935. doi:10.1007/s11517-006-0107-4 PubMedCrossRefGoogle Scholar
  33. 33.
    Suri JS, Farag AA, Micheli-Tzanakou E, Das B, Banerjee S (2007) Parametric contour model in medical image segmentation. In: Deformable models, topics in biomedical engineering. International Book Series, Springer New York, pp 31–74Google Scholar
  34. 34.
    Tanoori B, Azimifar Z, Shakibafar A, Katebi S (2011) Brain volumetry: An active contour model-based segmentation followed by svm-based classification. Comput Biol Med 41(8):619–632. doi:10.1016/j.compbiomed.2011.05.013 PubMedCrossRefGoogle Scholar
  35. 35.
    Tao XD, Chang MC (2010) A skull stripping method using deformable surface and tissue classification. In: Proceedings on SPIE medical imaging, vol 7623. doi:10.1117/12.844061
  36. 36.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Computer Vision. Sixth international conference IEEE. pp 839–846. doi:10.1109/ICCV.1998.710815
  37. 37.
    Tong CS, Yuen PC, Wong YY (2002) Dividing snake algorithm for multiple object segmentation. Opt Eng 41:3177–3182. doi:10.1117/1.1517289 CrossRefGoogle Scholar
  38. 38.
    Wang L, Li C, Sun Q, Xia D, Kao CY (2009) Active contours driven by local and global intensity fitting energy with application to brain {MR} image segmentation. Comput Med Imaging Graph 33(7):520–531. doi:10.1016/j.compmedimag.2009.04.010 PubMedCrossRefGoogle Scholar
  39. 39.
    Wang YH, Fu YL (2011) Research on segmentation methods of brain using mri images. In: 2011 international conference on energy and environmental science—ICEES 2011, IEEE, 11:2382–2388. doi:10.1016/j.egypro.2011.10.555
  40. 40.
    Wang YQ, Liu LX, Zhang H, Cao ZL, Lu SP (2010) Image segmentation using active contours with normally biased gvf external force. Signal Process Lett IEEE 17(10):875–878. doi:10.1109/LSP.2010.2060482 CrossRefGoogle Scholar
  41. 41.
    Xu CY, Prince J (1997) Gradient vector flow: a new external force for snakes. In: Computer Vision and Pattern Recognition. Proceedings on IEEE computer society conference. pp 66–71. doi:10.1109/CVPR.1997.609299
  42. 42.
    Yashil (2010) Fuzzy c-means clustering MATLAB toolbox. http://yashil.20m.com/. Accessed 28 Nov 2010

Copyright information

© International Federation for Medical and Biological Engineering 2013

Authors and Affiliations

  • Hung-Ting Liu
    • 1
  • Tony W. H. Sheu
    • 2
  • Herng-Hua Chang
    • 1
  1. 1.Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Engineering Science and Ocean EngineeringNational Taiwan UniversityTaipeiTaiwan

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