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New method for simultaneous moderate bias correction and image segmentation

New method for simultaneous moderate bias correction and image segmentation

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This study proposes a new method for simultaneous image segmentation and moderate bias correction. Though many methods are proposed to deal with the image intensity inhomogeneity, some problems still exist and have influenced the segmentation results a lot. In this study, a new model is proposed for image segmentation and correction based on the multiplicative intrinsic component optimization (MICO) model. First, the new model in the level set formulation for gray images has been presented and the split Bregman method for fast minimization has been applied. The proposed model is tested with lots of magnetic resonance images and some medical colour images with promising results. Experimental results show that the proposed model can simultaneously segment images and correct bias field moderately. In the experimental part for gray images, a qualitative comparison between the proposed model and the MICO model in both segmentation and bias-correction results is made. Besides, the proposed model with the Chan-Vese model and the illumination and reflectance estimation model in the experimental part for colour images are compared. Moreover, the proposed model can segment nature colour images successfully. It is clear that the proposed model has a good performance on many characteristics such as accuracy, efficiency, and robustness.

References

    1. 1)
      • 1. Kermi, A., Andjouh, K., Zidane, F.: ‘Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets’, IET. Image. Processing., 2018, 12, pp. 19641971.
    2. 2)
      • 2. Tavakoli, F., Ghasemi, J.: ‘Brain MRI segmentation by combining different MRI modalities using Dempster-Shafer theory’, IET. Image. Processing., 2018, 12, pp. 13221330.
    3. 3)
      • 3. Chen, Y.J., Wang, J.W., Mishra, A., et al: ‘Image segmentation and bias correction via an improved level set method’, Neurocomputing., 2011, 74, pp. 35203530.
    4. 4)
      • 4. Zhan, S., Yang, X.: ‘MR image bias field harmonic approximation with histogram statistical analysis’, Pattern Recognit. Lett.., 2016, 83, pp. 9198.
    5. 5)
      • 5. Wang, X., Huang, D., Xu, H.: ‘An efficient local Chan–Vese model for image segmentation’, IEEE Trans. Image. Process., 2010, 43, pp. 603618.
    6. 6)
      • 6. Li, C., Kao, C., Gore, J.C., et al: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE Trans. Image. Process., 2008, 17, pp. 19401949.
    7. 7)
      • 7. Lewis, E.B., Fox, N.C.: ‘Correction of differential intensity inhomogeneity in longitudinal MR images’, Neuroimage, 2004, 23, pp. 7583.
    8. 8)
      • 8. Meyer, C.R., Bland, P.H., Pipe, J.: ‘Retrospective correction of intensity inhomogeneities in MRI’, IEEE Trans. Med. Imag., 1995, 14, pp. 3641.
    9. 9)
      • 9. Millesa, J., Zhu, Y., Gimenezb, G., et al: ‘MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information’, Comput. Med. Imag. Graph., 2007, 31, pp. 8190.
    10. 10)
      • 10. Vemuri, P., Kholmovski, E.G., Parker, D.L., et al: ‘Coil sensitivity estimation for optimal snr reconstruction and intensity inhomogeneity correction in phased array MR imaging’. Proc. of the 19th Int. Conf. Information Processing in Medical Imaging, Colorado, USA, 2005, vol. 19, pp. 603614.
    11. 11)
      • 11. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: ‘A nonparametric method for automatic correction of intensity nonuniformity in MRI data’, IEEE Trans. Med. Imag., 1998, 17, pp. 8797.
    12. 12)
      • 12. Styner, M., Brechbuhler, C., Szckely, G., et al: ‘Parametric estimate of intensity inhomogeneities applied to MRI’, IEEE Trans. Med. Imag., 2000, 19, pp. 153165.
    13. 13)
      • 13. Yin, W., Osher, S., Goldfarb, D., et al: ‘Bregman iterative algorithms for L1-minimization with applications to compressed sensing’, SIAM J. Imag. Sci., 2008, 29, pp. 143168.
    14. 14)
      • 14. Li, C., Gore, J.C., Davatzikosa, C.: ‘Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation’, Magn. Reson. Imag., 2014, 32, pp. 913923.
    15. 15)
      • 15. Goldstein, T., Osher, S.: ‘The split Bregman method for L1 regularized problems SIAM’, J. Imag. Sci., 2009, 49, pp. 323343.
    16. 16)
      • 16. Dodangeh, M., Figueiredo, I.N., Goncalves, G.: ‘Spatially adaptive total variation deblurring with split Bregman technique’, IET Image Process.., 2018, 12, pp. 948958.
    17. 17)
      • 17. Yang, Y., Li, C., Kao, C., et al: ‘Split Bregman method for minimization of region-scalable fitting energy for image segmentation’. Proc. of Int. Symp. Visual Computing, Las Vegas, USA, 2010, vol. 6454, pp. 117128.
    18. 18)
      • 18. He, L.T., Wang, Y.L.: ‘Iterative support detection-based split Bregman method for wavelet frame-based image inpainting’, IEEE Trans. Image. Process., 2014, 23, pp. 54705485.
    19. 19)
      • 19. Li, C., Li, F., Kao, C.Y., et al: ‘Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach’. 2009 IEEE 12th Int. Conf. Computer Vision (ICCV), Kyoto, Japan, 2009, pp. 702708.
    20. 20)
      • 20. Brllgman, L.M.: ‘The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming’, USSR Comp. Math. Math. Phys., 1967, 7, pp. 200217.
    21. 21)
      • 21. Chen, Y., Vemuri, B.C., Wang, L.: ‘Image denoising and segmentation via nonlinear diffusion’, J. Comput. Appl. Math., 2000, 39, pp. 131149.
    22. 22)
      • 22. Sammouda, R., Adgaba, N., Touir, A., et al: ‘Agriculture satellite image segmentation using a modified artificial Hopfield neural network’, Comput. Hum. Behav., 2014, 30, pp. 436441.
    23. 23)
      • 23. Jia, H., Yap, P.T., Shen, D.: ‘Iterative multi-atlas-based multi-image segmentation with tree-based registration’, Comput. Hum. Behav., 2011, 59, pp. 422430.
    24. 24)
      • 24. Johnson, F., Sharma, A.: ‘Iterative multi-atlas-based multi-image segmentation with tree-based registration’, J. Hydrol., 2015, 525, pp. 472485.
    25. 25)
      • 25. Guillemaud, R., Brady, M.: ‘Estimating the bias field of MR images’, IEEE Trans. Med. Imag., 1997, 32, pp. 238251.
    26. 26)
      • 26. Van Leemput, K., Maes, F., Vandermeulen, D., et al: ‘Automated model-based bias field correction of MR images of the brain’, IEEE Trans. Med. Imag., 1999, 18, pp. 885896.
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