Advertisement

Journal of Digital Imaging

, Volume 28, Issue 6, pp 738–747 | Cite as

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images

  • Simona Moldovanu
  • Luminița Moraru
  • Anjan Biswas
Article

Abstract

This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu’s, Sauvola’s, Niblack’s, and Bernsen’s binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

Keywords

Skull stripping Irrational mask Binarization Similarity metrics Magnetic resonance image 

Notes

Acknowledgments

The author Simona Moldovanu would like to thank the Project PERFORM, ID POSDRU/159/1.5/S/138963 of “Dunărea de Jos” University of Galaţi, Romania, for support.

References

  1. 1.
    Rajendran A, Dhanasekaran R: Brain tumor segmentation on MRI brain images with fuzzy clustering and GVF snake model. Int J Comput Commun 7:530–539, 2012CrossRefGoogle Scholar
  2. 2.
    Rajendran A, Dhanasekaran R: A Combined Method Using fuzzy clustering and MGGVF snake model for brain tumor segmentation on MRI image. JGRCS 2:1–5, 2011Google Scholar
  3. 3.
    Guoqiang W, Dongxue W: Segmentation of brain MRI image with GVF snake model. In: Proceedings of the 1st International Conference on Pervasive Computing Signal Processing and Applications, Harbin, China, 2010, pp. 711–714Google Scholar
  4. 4.
    Moreno JC, Prasath VBS, Proença H, Palaniappan K: Fast and globally convex multiphase active contours for brain MRI segmentation. Comput Vis Image Underst 125:237–250, 2014CrossRefGoogle Scholar
  5. 5.
    Tirpud N, Welekar R: Automated detection and extraction of brain tumor from MRI images. Int J Comput Appl 77:26–30, 2013Google Scholar
  6. 6.
    Seghier ML, Kolanko MA, Leff AP, Jager HR, Gregoire SM, Werring DJ: Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images. PLoS ONE 6:1–9, 2011CrossRefGoogle Scholar
  7. 7.
    Bandhyopadhyay SK, Paul TU: Segmentation of brain MRI image – a review. IJARCSEE 2:409–413, 2012Google Scholar
  8. 8.
    Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol 10:1341–1348, 2003PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Vijayakumar C, Gharpure DC: Development of image-processing software for automatic segmentation of brain tumors in MR images. Med Phys 36:147–158, 2011CrossRefGoogle Scholar
  10. 10.
    Punga M, Gaurav R, Moraru L: Level set method coupled with energy image features for brain MR image segmentation. Biomed Tech 59:219–229, 2014CrossRefGoogle Scholar
  11. 11.
    Sadananthan SA, Zheng W, Chee MWL, Zagorodnov V: Skull stripping using graph cuts. Neuroimage 49:225–239, 2010CrossRefPubMedGoogle Scholar
  12. 12.
    Hahn HK, Peitgen HO: The skull stripping in MRI solved by single 3D Watershed transform, in: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention Berlin, Germany, 2000, pp. 134–143Google Scholar
  13. 13.
    Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B: A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060–1075, 2004CrossRefPubMedGoogle Scholar
  14. 14.
    Galdames FJ, Jaillet F, Perez CA: An accurate skull stripping method based on simplex meshes and analysis in magnetic resonance images. J Neurosci Methods 206:103–119, 2012CrossRefPubMedGoogle Scholar
  15. 15.
    Doshi J, Erus G, Ou Y, Gaonkar B, Davatzikos C: Multi-Atlas skull-stripping. Acad Radiol 20:1566–1576, 2013CrossRefPubMedGoogle Scholar
  16. 16.
    Ramesh M, Priya P, Arabi PM: A novel approach for efficient skull stripping using morphological reconstruction a thresholding. IJRET 3:96–101, 2014Google Scholar
  17. 17.
    Somasundaram K, Kalavathi P: Skull stripping of MRI head scans based on Chan-Vese active contour model. KM&EL 3:7–14, 2011Google Scholar
  18. 18.
    Mirajkar G, Patil S, Pawar M: Skull stripping using geodesic active contours in magnetic resonance images. In: Proceedings of the 4th International Conference on Computational Intelligence, Communication Systems and Networks, Phuket, Thailand, 2012, pp. 301–306Google Scholar
  19. 19.
    Mahapatra D: Skull stripping of neonatal brain MRI: using prior shape information with graph cuts. J Digit Imaging 25:802–814, 2012PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.
    Krishnan NP, Kenkre N, Nancy NS: Tumor detection using threshold operation in MRI brain images. In: Proceedings of the International Conference Computational Intelligence & Computing Research, (December 2012), Coimbatore, India, pp. 1–4Google Scholar
  21. 21.
    George EB, Karnan M: MRI Brain Image enhancement using filtering techniques. IJCSET 3:399–403, 2012Google Scholar
  22. 22.
    Kanimozhi M, Bindu CH: Brain MR image segmentation using self organizing map. IJARCCE 2:3968–3973, 2013Google Scholar
  23. 23.
    Mohan J, Krishnaveni V, Guo Y: MRI denoising using nonlocal neutrosophic set approach of Wiener filtering. Biomed Signal Process 8:779–791, 2013CrossRefGoogle Scholar
  24. 24.
    Balafar MA, Ramli AR, Saripan MI, Mashohor S: Review of brain MRI image segmentation methods. Artif Intell Rev 33:261–274, 2010CrossRefGoogle Scholar
  25. 25.
    Otsu N: A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 9:62–66, 1979CrossRefGoogle Scholar
  26. 26.
    Sauvola J, Pietikainen M: Adaptive document image binarization. Pattern Recogn 33:225–236, 2000CrossRefGoogle Scholar
  27. 27.
    Niblack W: An Introduction to Digital Image Processing. Prentice Hall, Denmark, 1986Google Scholar
  28. 28.
    Bernsen J: Dynamic thresholding of gray level images. In: Proceedings of the 8th International Conference on Pattern Recognition, (October 1986), Paris, France pp. 1251–1255Google Scholar
  29. 29.
    Shattuck DW, Sandor-Leahy SR, Shaper KA, Rottenberg DA, Leahy RM: Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13:856–876, 2001CrossRefPubMedGoogle Scholar
  30. 30.
    Debnath L: The Legacy of Leonhard Euler: A Tricentennial Tribute. Imperial College Press, London, 2010Google Scholar
  31. 31.
    Anbeek P, Vincken KL, Van Osch MJP, Bisschops RHC, Van Der Grond J: Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 21:1037–1044, 2004CrossRefPubMedGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2015

Authors and Affiliations

  • Simona Moldovanu
    • 1
    • 2
  • Luminița Moraru
    • 1
  • Anjan Biswas
    • 3
    • 4
  1. 1.Department of Chemistry, Physics and Environment, Faculty of Sciences and EnvironmentDunărea de Jos University of GalaţiGalaţiRomania
  2. 2.Dumitru Moţoc High SchoolGalaţiRomania
  3. 3.Department of Mathematical SciencesDelaware State UniversityDoverUSA
  4. 4.Department of Mathematics, Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia

Personalised recommendations