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Local energy-based multimodal medical image fusion in curvelet domain

Local energy-based multimodal medical image fusion in curvelet domain

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Various multimodal medical images like computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography, single photon emission CT and structural MRI have different characteristics and carry different types of complementary anatomical and functional information. Therefore, fusion of multimodal images is required, in order to achieve good spatial resolution images carrying both anatomical and functional information. In this work, the authors have proposed a fusion technique based on curvelet transform. Curvelet transform is a multiscale, multidirectional transform having anisotropic property and is very efficient in capturing edge points in images. Edges in an image are the important information carrying points used to show better visual structure of the image. They use local energy-based fusion rule which is more effective than single pixel-based fusion rules. Comparison of the proposed method with other existing spatial and wavelet transform based methods, in terms of visual and quantitative measures show the effectiveness of the proposed method. For quantitative analysis of the method, they used five fusion metrics as entropy, standard deviation, edge-strength, sharpness and average gradient.

References

    1. 1)
      • 1. James, A.P., Dasarathy, B.V.: ‘Medical image fusion: a survey of the state-of-the-art’, Inf. Fusion, 2014, 19, pp. 419.
    2. 2)
      • 2. Barra, V., Boire, J.V.: ‘A general framework for the fusion of anatomical and functional medical images’, NeuroImage, 2001, 13, (3), pp. 410424.
    3. 3)
      • 3. Khare, A., Tiwari, U.S.: ‘Soft-thresholding for denoising of medical images—A multiresolution approach’, Int. J. Wavelets, Multiresolution Inf. Process., 2005, 3, (4), pp. 477496.
    4. 4)
      • 4. Khare, A., Tiwari, U.S., Jeon, M.: ‘Daubechies complex wavelet transform based multilevel shrinkage for deblurring of medical images in presence of noise’, Int. J. Wavelets, Multiresolution Inf. Process., 2009, 7, (5), pp. 587604.
    5. 5)
      • 5. Khalegi, B., Khamis, A., Karray, F.O., et al: ‘Multisensor data fusion: a review of the state-of-the-art’, Inf. Fusion, 2013, 14, (1), pp. 2844.
    6. 6)
      • 6. Yang, B., Li, S.: ‘Pixel level image fusion with simultaneous orthogonal matching pursuit’, Inf. Fusion, 2012, 13, (1), pp. 1019.
    7. 7)
      • 7. Yang, J., Zang, X.: ‘Feature-level fusion of fingerprint and finger-vein for personal identification’, Pattern Recognit. Lett., 2012, 33, (5), pp. 623628.
    8. 8)
      • 8. Tao, Q., Veldhuis, R.: ‘Threshold-optimized decision-level fusion and its application to biometrics’, Pattern Recognit., 2009, 42, (5), pp. 823836.
    9. 9)
      • 9. Wan, T., Zhu, C., Qin, Z.: ‘Multifocus image fusion based on robust principal component analysis’, Pattern Recognit. Lett., 2013, 34, (9), pp. 10011008.
    10. 10)
      • 10. Burt, P.J., Adelson, E.H.: ‘The laplacian pyramid as a compact image code’, IEEE Trans. Commun., 1983, 31, (4), pp. 532540.
    11. 11)
      • 11. Li, H., Manjunath, B.S., Mitra, S.K.: ‘Multisensor image fusion using wavelet transform’, Graph. Models Image Process., 1995, 57, (3), pp. 235245.
    12. 12)
      • 12. Nehcini, F., Garzelli, A., Baronti, S., et al: ‘Remote sensing image fusion using the curvelet transform’, Inf. Fusion, 2007, 8, (2), pp. 143156.
    13. 13)
      • 13. Li, S., Yang, B.: ‘Multifocus image fusion by combining curvelet and wavelet transform’, Pattern Recognit. Lett., 2008, 29, (9), pp. 12951130.
    14. 14)
      • 14. Wang, W., Chang, F.: ‘A multi-focus image fusion method based on Laplacian pyramid’, J. Comput., 2011, 6, (12), pp. 25592566.
    15. 15)
      • 15. Candes, E.J., Donoho, D.L.: ‘Continuous curvelet transform: I. Resolution of the wavefront set’, Appl. Comput. Harmon. Anal., 2005, 19, (2), pp. 162197.
    16. 16)
      • 16. Candes, E.J., Donoho, D.L.: ‘Continuous curvelet transform: II. Discretization and frames’, Appl. Comput. Harmon. Anal., 2005, 19, (2), pp. 198222.
    17. 17)
      • 17. Candes, E., Demanet, L., Donoho, D., et al: ‘Fast discrete curvelet transforms’, Multiscale Model. Simul., 2006, 5, (3), pp. 861899.
    18. 18)
      • 18. Binh, N.T., Khare, A.: ‘Multilevel threshold based image denoising in curvelet domain’, J. Comput. Sci. Technol., 2010, 25, (3), pp. 632640.
    19. 19)
      • 19. Binh, N.T., Khare, A.: ‘Object tracking of video sequences in curvelet domain’, Int. J. Image Graph., 2011, 11, (1), pp. 120.
    20. 20)
      • 20. Khare, M., Srivastava, R.K., Khare, A., et al: ‘Curvelet transform based moving object segmentation’. Proc. of 20th IEEE Int. Conf. on Image Processing (ICIP 2013), Melbourne, Australia, September 2013, pp. 40794083.
    21. 21)
      • 21. Mahyari, A.G., Yazdi, M.: ‘A noval image fusion method using curvelet transform based on linear dependency test’. Proc. Int. Conf. Digital Image Processing, Bangkok, Thailand, March 2009, pp. 351354.
    22. 22)
      • 22. Singh, R., Khare, A.: ‘Fusion of multimodal medical images using Daubechies complex wavelet transform – a multiresolution approach’, Inf. Fusion, 2014, 19, pp. 4960.
    23. 23)
      • 23. Srivastava, R., Singh, R., Khare, A.: ‘Fusion of multifocus noisy images using contourlet transform’. Proc. 6th Int. Conf. Contemporary Computing, Noida, India, August 2013, pp. 497502.
    24. 24)
      • 24. Donoho, D.L., Flesia, A.G.: ‘Digital ridgelet transform based on true ridge functions’. inStoeckler, J., Welland, G.V. (Eds.): ‘Beyond wavelets’ (Academic Press, 2002, 1st edn.), pp. 130.
    25. 25)
      • 25. Tang, L., Zhao, F., Zhao, Z.: ‘The nonsubsampled contourlet transform for image fusion’. Proc. Int. Conf. Wavelet Analysis and Pattern Recognition, Beijing, China, November 2007, pp. 305310.
    26. 26)
      • 26. Srivastava, R., Khare, A.: ‘Medical image fusion using local energy in nonsubsampled contourlet transform domain’. Proc. 5th Int. Conf. Computational Vision and Robotics, August 2014, pp. 2935.
    27. 27)
      • 27. Huimin, L., Yujie, L., Kitazono, Y., et al: ‘Local energy based multi-focus image fusion on curvelet transform’. Proc. Int. Symp. on Communication and Information Technology (ISCIT), October 2010, pp. 11541157.
    28. 28)
      • 28. Yang, B., Li, S., Sun, F.: ‘Image fusion using nonsubsampled contourlet transform’. Proc. 4th Int. Conf. Image and Graphics, 2007, pp. 719724.
    29. 29)
      • 29. Li, S., Li, Z., Gong, J.: ‘Multivariate statistical analysis of measure for assessing the quality of image fusion’, Int. J. Image Data Fusion, 2010, 1, (1), pp. 4766.
    30. 30)
      • 30. Shi, W., Zhu, C., Tian, Y., et al: ‘Wavelet based image fusion and quality assessment’, Int. J. Appl. Earth Obs. Geo Inf., 2005, 6, (3), pp. 241251.
    31. 31)
      • 31. Tian, J., Chen, L., Ma, L., et al: ‘Multi-focus image fusion using a bilateral gradient-based sharpness criterion’, Opt. Commun., 2011, 284, (1), pp. 8087.
    32. 32)
      • 32. Borwonwatanadelok, P., Rattanapitak, W., Udomhunsakul, S.: ‘Multi-focus image fusion based on stationary wavelet transform and extended spatial frequency measurement’. Proc. Int. Conf. Electronic Computer Technology, Macau, China, February 2009, pp. 7781.
    33. 33)
      • 33. Deng, A., Wu, J., Yang, S.: ‘An image fusion algorithm based on discrete wavelet transform and Canny operator’. Int. Conf. Advance Research on Computer Education, Simulation and Modeling, Wuhan, China, June 2011, pp. 3238.
    34. 34)
      • 34. Lewis, J.J., O'Callaghan, R.J., Nikolov, S.G., et al: ‘Pixel- and region-based image fusion with complex wavelets’, Inf. Fusion, 2007, 8, (2), pp. 119130.
    35. 35)
      • 35. Xiaohong, X., Zhihong, W.: ‘Image fusion based on Lifting wavelet transform’. Proc. Int. Symp. on Intelligence Information Processing and Trusted Computing (IPTC), Huanggang, China, October 2010, pp. 659662.
    36. 36)
      • 36. Wang, H.: ‘A new multiwavelet-based approach to image fusion’, J. Math. Imaging Vis., 2004, 21, (2), pp. 177192.
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