A Hybrid Approach to Brain Extraction from Premature Infant MRI

  • Michèle Péporté
  • Dana E. Ilea Ghita
  • Eilish Twomey
  • Paul F. Whelan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

This paper describes a novel automatic skull-stripping method for premature infant data. A skull-stripping approach involves the removal of non-brain tissue from medical brain images. The new method reduces the image artefacts, generates binary masks and multiple thresholds, and extracts the region of interest. To define the outer boundary of the brain tissue, a binary mask is generated using morphological operators, followed by region growing and edge detection. For a better accuracy, a threshold for each slice in the volume is calculated using k-means clustering. The segmentation of the brain tissue is achieved by applying a region growing and finalized with a local edge refinement. This technique has been tested and compared to manually segmented data and to four well-established state of the art brain extraction methods.

Keywords

Skull Stripping Newborns MRI Brain Segmentation 

References

  1. 1.
    Babalola, K.O., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T., Jenkinson, M., Rueckert, D.: An evaluation of four automatic methods of segmenting the subcortical structures in the brain. NeuroImage 47, 1435–1447 (2009)CrossRefGoogle Scholar
  2. 2.
    Balan, A.G.R., Traina, A.J.M., Ribeiro, M.X., Marques, P.M.A., Traina Jr., C.: Head: The Human Encephalon Automatic Delimiter. In: CBMS 2007: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems, pp. 171–176. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  3. 3.
    Boesen, K., Rehm, K., Shaper, K., Stoltzner, S., Lueders, E., Rottenberg, D.: Quantitative comparison of four brain extraction algorithms. NeuroImage 22, 1255–1261 (2004)CrossRefGoogle Scholar
  4. 4.
    Chiverton, J., Wells, K., Lewis, E., Chen, C., Podda, B., Johnson, D.: Statistical morphological skull stripping of adult and infant MRI data. Computers in Biology and Medicine 37, 342–357 (2007)CrossRefGoogle Scholar
  5. 5.
    Crum, W.R., Rueckert, D., Jenkinson, M., Kennedy, D., Smith, S.M.: A framework for detailed objective comparison of non-rigid registration algorithms in neuroimaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 679–686. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Friston, K.J., Penny, W.: Posterior probability maps and SPMs. NeuroImage 19, 1240–1249 (2003)CrossRefGoogle Scholar
  7. 7.
    Hahn, H.K., Peitgen, H.-O.: The skull stripping problem in MRI solved by a single 3D watershed transform. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 134–143. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Harnsberger, H.R., Osborn, A.G., Ross, J., Macdonald, A.: Diagnostic and Surgical Imaging Anatomy: Brain, Head and Neck, Spine. Amirsys Inc. (2006)Google Scholar
  9. 9.
    Kobashi, S., Fujimoto, Y., Ogawa, M., Ando, K., Ishikura, R., Kondo, K., Hirota, S., Hata, Y.: Fuzzy-ASM Based Automated Skull Stripping Method from Infantile Brain MR Images. In: IEEE International Conference on Granular Computing, pp. 632–635 (2007)Google Scholar
  10. 10.
    Li, X.: CI, L., Wang, R., Li, J.: A Region Growing Method Based on Fuzzy Connectedness. In: ICALIP, pp. 993–997 (2008)Google Scholar
  11. 11.
    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of Royal Society of London 207(B), 187–217 (1980)CrossRefGoogle Scholar
  12. 12.
    Mathur, A.M., Neil, J.J., Inder, T.E.: Understanding Brain Injury and Neurodevelopment Disabilities in the Premature Infant: The Evolving Role of Advanced Magnetic Resonance Imagine. Seminar in Perinatology 34, 57–66 (2010)CrossRefGoogle Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-Spacing and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Pohle, R., Toennies, K.D.: Segmentation of medical images using adaptive region growing. Proceedings of SPIE 4322, 1337–1346 (2001)CrossRefMATHGoogle Scholar
  15. 15.
    Prastawa, M., Gilmore, J.H., Lin, W., Gerig, G.: Automatic segmentation of MR image of the developing newborn brain. Medical Image Analysis 9, 457–466 (2005)CrossRefGoogle Scholar
  16. 16.
    Rehm, K., Schaper, K., Anderson, J., Woods, R.: Putting our heads together: a consensus approach to brain/non–brian segmentation in T1–weighted MR volumes. NeuroImage 22, 1262–1270 (2004)CrossRefGoogle Scholar
  17. 17.
    Rorden, C., Brett, M.: Stereotaxic display of brain lessions. Behavioural Neurology 12, 191–200 (2000)CrossRefGoogle Scholar
  18. 18.
    Sadananthan, S.A., Zheng, W., Chee, M.W., Zagorodnov, V.: Skull stripping using graph cuts. NeuroImage 49, 225–239 (2010)CrossRefGoogle Scholar
  19. 19.
    Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B.: A hybrid approach to the skull stripping problem in MRI. NeuroImage 22, 1060–1075 (2004)CrossRefGoogle Scholar
  20. 20.
    Shanthi, K., Sasi Kumar, M.: Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques. In: International Conference on Intelligent and Advanced Systems, ICIAS 2007, November 25-28, pp. 422–426. IEEE Computer Society, Los Alamitos (2007)CrossRefGoogle Scholar
  21. 21.
    Shattuck, D.W., Leathy, R.M.: BrainSuite: An automated cortical surface identification tool. Medical Image Analysis 6, 129–142 (2002)CrossRefGoogle Scholar
  22. 22.
    Shattuck, D.W., Sandor-Leathy, S.R., Shaper, K.A., Rottenberg, D.A., Leathy, R.M.: Magnetic Resonance Image Tissue Classification Using a Partial Volume Model. NeuroImage 13, 856–876 (2001)CrossRefGoogle Scholar
  23. 23.
    Smith, S.M.: Fast robust automated brain extraction. Human Brain Mapping 17, 143–155 (2002)CrossRefGoogle Scholar
  24. 24.
    Smith, S., Jenkinson, M., Woolrich, M., Beckmann, C., Behrens, T., Johansen-Berg, H., Bannister, P., Luca, M.D., Drobnjak, I., Flitney, D., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., Stefano, N.D., Brady, J., Matthews, P.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(S1), 208–219 (2004)CrossRefGoogle Scholar
  25. 25.
    SPM8: This software is available at the web address, http://www.fil.ion.ucl.ac.uk/spm/
  26. 26.
    Tzaroushi, L.C., Astrakas, L.G., Zikou, A., Xydis, V., Kosta, P., Andronikou, S., Argyropoulou, M.I.: Preventricular leukomalacia in preterm children: assessment of grey and white matter and cerebrospinal fluid changes by MRI. Pediatric Radiology 39, 1327–1332 (2009)CrossRefGoogle Scholar
  27. 27.
    Weickert, J.: Coherence–Enhancing Diffusion Filtering. Internation Journal of Computer Vision 31(2/3), 111–127 (1999)CrossRefGoogle Scholar
  28. 28.
    Zhao, W., Xie, M., Gao, J., Li, T.: A Modified Skull-Stripping Method Based on Morphological Processing. In: ICCMS 2010: Second International Conference on Computer Modeling and Simulation, vol. 1, pp. 159–163 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michèle Péporté
    • 1
  • Dana E. Ilea Ghita
    • 1
  • Eilish Twomey
    • 2
  • Paul F. Whelan
    • 1
  1. 1.Centre for Image Processing and AnalysisDublin City UniversityIreland
  2. 2.Department of RadiologyChildrens University HospitalDublinIreland

Personalised recommendations