access icon free Mid-sagittal plane detection in brain magnetic resonance image based on multifractal techniques

Human brain is separated into two hemispheres by the mid-sagittal plane (MSP) as bilateral symmetry. Extraction of this symmetry plane from magnetic resonance images is one of the precise processes for diagnosis. The foremost challenge of this work is to analyse the degree of asymmetry between hemispheres. Most of the existing work has analysed primarily on the image intensity to estimate the asymmetry between hemispheres. The present study explores the possibility of the generalised fractal dimensions to measure the asymmetry between hemispheres, in addition multifractal spectra applies to refine the optimal region of interest which characterises the complexity and homogeneity of an object. In order to validate the efficiency of the proposed technique, experimental results are compared with three state-of-the-art methods by the performance evaluation metrics such as yaw angle error and roll angle error. Besides, angular deviation and average deviation of distance between ground truth line and extracted MSP by the developed method is compared.

Inspec keywords: fractals; brain; biomedical measurement; biomedical MRI; medical image processing

Other keywords: MSP; brain magnetic resonance image; angular deviation; multifractal techniques; midsagittal plane detection; asymmetry

Subjects: Biomedical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation; Medical magnetic resonance imaging and spectroscopy; Biophysics of neurophysiological processes; Fractals; Biology and medical computing; Computer vision and image processing techniques

References

    1. 1)
      • 5. Gordillo, N., Montseny, E., Sobrevilla, P.: ‘State of the art survey on MRI brain tumor segmentation’, Magn. Reson. Imaging, 2013, 31, pp. 14261438.
    2. 2)
      • 26. http:www.bic.mni.mcgill.cabrainweb.
    3. 3)
      • 22. Perrier, E., Tarquis, A.M., Dathe, A.: ‘A program for fractal and multifractal analysis of two-dimensional binary images: computer algorithms versus mathematical theory’, Geoderma, 2006, 134, pp. 284294.
    4. 4)
      • 13. Mandelbrot, B.B.: ‘The fractal geometry of nature’ (W.H. Freeman and Company, New York, 1983).
    5. 5)
      • 15. Hentschel, H.G.E., Procaccia, I.: ‘The infinite number of generalized dimensions of fractals and strange attractors’, Physica 8D, 1983, 8, (3), pp. 435444.
    6. 6)
      • 19. Zook, J.M., Iftekharuddin, K.M.: ‘Statistical analysis of fractal based brain tumor detection algorithms’, Magn. Reson. Imaging, 2005, 23, pp. 671678.
    7. 7)
      • 10. Ruppert, G.C.S., Teverovskiy, L.A., Yu, C.P., et al: ‘A new symmetry-based method for mid-sagittal plane extraction in neuroimages’. IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, 2011, pp. 285288.
    8. 8)
      • 23. Souza, J.D., Rostirolla, S.P.: ‘A fast MATLAB program to estimate the multifractal spectrum of multidimensional data: application to fractures’, Comput. Geosci., 2011, 37, pp. 241249.
    9. 9)
      • 18. Uthayakumar, R., Gowrisankar, A.: ‘Generalized fractal dimensions in image thresholding technique’, Inf. Sci. Lett., 2014, 3, pp. 110.
    10. 10)
      • 20. Renyi, A.: ‘On a new axiomatic theory of probability’, Acta Math. Hung., 1955, 6, pp. 285335.
    11. 11)
      • 17. Easwaramoorthy, D., Uthayakumar, R.: ‘Improved generalized fractal dimensions in the discrimination between healthy and epileptic EEG signals’, J. Comput. Sci., 2011, 2, pp. 3138.
    12. 12)
      • 8. Tuzikov, A.V., Colliot, O., Bloch, I.: ‘Evaluation of the symmetry plane in 3D MR brain images’, Pattern Recognit. Lett., 2003, 24, pp. 22192233.
    13. 13)
      • 2. Suri, J.S.: ‘Two-dimensional fast magnetic resonance brain segmentation’, IEEE Eng. Med. Biol. Mag, 2001, 20, pp. 8495.
    14. 14)
      • 6. Kruggel, F., Cramon, D.Y.: ‘Alignment of magnetic-resonance brain datasets with the stereotactical coordinate system’, Med. Image Anal., 1999, 3, (2), pp. 175185.
    15. 15)
      • 11. Zhang, Y., Hu, Q.: ‘A PCA-based approach to the representation and recognition of MR brain mid sagittal plane images’. Thirtieth Annual Int. IEEE EMBS, 2008, pp. 39163919.
    16. 16)
      • 12. Jayasuriya, S.A., Liew, A.W.C., Law, N.F.: ‘Brain symmetry plane detection based on fractal analysis’, Comput. Med. Imaging Graph, 2013, 37, pp. 568580.
    17. 17)
      • 25. http:www.cma.mgh.harvard.eduibsr.
    18. 18)
      • 21. Grassberger, P.: ‘Generalized dimensions of strange attractors’, Phys. Lett. A, 1983, 97, pp. 227320.
    19. 19)
      • 24. Harte, D.: ‘Multifractals’ (Chapman and Hall, London, 2001), ISBN 978-1-58488-154-4.
    20. 20)
      • 4. Rigaut, J.P.: ‘Automated image segmentation by mathematical morphology and fractal geometry’, J. Microsc., 1988, 150, pp. 2130.
    21. 21)
      • 3. Uemura, K., Toyama, H., Baba, S., et al: ‘Generation of fractal dimension images and its application to automatic edge detection in brain MRI’, Comput. Med. Imaging Graph, 2000, 24, pp. 7385.
    22. 22)
      • 7. Liu, Y., Collins, R.T., Rothfus, W.E.: ‘Robust mid sagittal plane extraction from normal and pathological 3D neuroradiology images’, IEEE Trans. Med. Imaging, 2001, 20, (3), pp. 175192.
    23. 23)
      • 9. Liu, S.X., Kender, J., Imielinska, C., et al: ‘Employing symmetry features for automatic misalignment correction in neuroimages’, J. Neuroimaging, 2011, 21, pp. 1533.
    24. 24)
      • 16. Iftekharuddin, K.M., Jia, W., March, R.: ‘Fractal analysis of tumor in brain MR images’, Mach. Vis. Appl., 2003, 13, pp. 352362.
    25. 25)
      • 14. Lopes, R., Betrouni, N.: ‘Fractal and multifractal analysis: a review’, Med. Image Anal., 2009, 13, pp. 634649.
    26. 26)
      • 1. Jimenez-Alaniz, J.J., Medina-Bañuelos, V., Yáñez-Suárez, O.: ‘Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information’, IEEE Trans. Med. Imaging, 2005, 25, pp. 7483.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0003
Loading

Related content

content/journals/10.1049/iet-ipr.2016.0003
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading