access icon free New method for simultaneous moderate bias correction and image segmentation

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.

Inspec keywords: biomedical MRI; image segmentation; medical image processing; image colour analysis; minimisation

Other keywords: illumination estimation model; multiplicative intrinsic component optimisation model; Chan–Vese model; grey magnetic resonance images; reflectance estimation model; bias field correction; medical colour images; split Bregman method; image intensity inhomogeneity

Subjects: Patient diagnostic methods and instrumentation; Optical, image and video signal processing; Medical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Biology and medical computing; Biomedical magnetic resonance imaging and spectroscopy

References

    1. 1)
      • 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.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 24. Johnson, F., Sharma, A.: ‘Iterative multi-atlas-based multi-image segmentation with tree-based registration’, J. Hydrol., 2015, 525, pp. 472485.
    5. 5)
      • 15. Goldstein, T., Osher, S.: ‘The split Bregman method for L1 regularized problems SIAM’, J. Imag. Sci., 2009, 49, pp. 323343.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • 16. Dodangeh, M., Figueiredo, I.N., Goncalves, G.: ‘Spatially adaptive total variation deblurring with split Bregman technique’, IET Image Process.., 2018, 12, pp. 948958.
    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)
      • 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.
    13. 13)
      • 4. Zhan, S., Yang, X.: ‘MR image bias field harmonic approximation with histogram statistical analysis’, Pattern Recognit. Lett.., 2016, 83, pp. 9198.
    14. 14)
      • 8. Meyer, C.R., Bland, P.H., Pipe, J.: ‘Retrospective correction of intensity inhomogeneities in MRI’, IEEE Trans. Med. Imag., 1995, 14, pp. 3641.
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 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.
    18. 18)
      • 2. Tavakoli, F., Ghasemi, J.: ‘Brain MRI segmentation by combining different MRI modalities using Dempster-Shafer theory’, IET. Image. Processing., 2018, 12, pp. 13221330.
    19. 19)
      • 7. Lewis, E.B., Fox, N.C.: ‘Correction of differential intensity inhomogeneity in longitudinal MR images’, Neuroimage, 2004, 23, pp. 7583.
    20. 20)
      • 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.
    21. 21)
      • 25. Guillemaud, R., Brady, M.: ‘Estimating the bias field of MR images’, IEEE Trans. Med. Imag., 1997, 32, pp. 238251.
    22. 22)
      • 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.
    23. 23)
      • 21. Chen, Y., Vemuri, B.C., Wang, L.: ‘Image denoising and segmentation via nonlinear diffusion’, J. Comput. Appl. Math., 2000, 39, pp. 131149.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 5. Wang, X., Huang, D., Xu, H.: ‘An efficient local Chan–Vese model for image segmentation’, IEEE Trans. Image. Process., 2010, 43, pp. 603618.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5171
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5171
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading