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Non-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Non-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

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Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y-component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

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. Herrmann, K.A., Kohan, A.A., Gaeta, M.C., et al: ‘PET/MRI: applications in clinical imaging’, Curr. Cardiol. Rep., 2013, 1, (3), pp. 161176.
    3. 3)
      • 3. Javed, U., Riaz, M.M., Ghafoor, A., et al: ‘MRI and PET image fusion using fuzzy logic and image local features’, Sci. World J., 2014, 2014.
    4. 4)
      • 4. Haddadpour, M., Daneshavar, S., Seyedarabi, H.: ‘PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method’, Biomed. J., 2017, 40, (4), pp. 219225.
    5. 5)
      • 5. Brundashree, R., Kakhandaki, N., Kulkarni, S.B., et al: ‘The PET-MRI brain image fusion using wavelet transforms’, Int. J. Eng. Trends Technol., 2015, 23, pp. 304307.
    6. 6)
      • 6. Rana, D. H., Degadwala, S.D.: ‘Medical image fusion using combined multi-resolution and multi-scaling transform’, Int. J. Comput. Appl., 2014, 107, (6), pp. 2629.
    7. 7)
      • 7. Shaveta, M., Arpinder, S.: ‘A comparative analysis of different image fusion techniques’, Int. J. Comput. Sci., 2014, 2, (1), pp. 815.
    8. 8)
      • 8. Li, S., Yang, B., Hu, J.: ‘Performance comparison of different multi resolution transforms for image fusion’, Inf. Fusion, 2011, 12, pp. 7484.
    9. 9)
      • 9. Li, T., Wang, Y.: ‘Biological image fusion using a NSCT based variable-weight method’, Inf. Fusion, 2011, 12, (2), pp. 8592.
    10. 10)
      • 10. Miao, Q., Shi, C., Xu, P., et al: ‘A novel algorithm of image fusion using shearlets’, Opt. Commun., 2011, 284, pp. 15401547.
    11. 11)
      • 11. Wang, L., Li, B., Tian, L.F.: ‘Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients’, Inf. Fusion, 2012, 17, pp. 19.
    12. 12)
      • 12. Cheng, S., Qiguangn, M., Pengfei, X.: ‘A novel algorithm of remote sensing image fusion based on shearlets and PCNN’, Neurocomputing, 2013, 117, pp. 4757.
    13. 13)
      • 13. Kong, W.W.: ‘Multi-sensor image fusion based on NSST domain I2CM’, Electron. Lett., 2013, 49, (13), pp. 802803.
    14. 14)
      • 14. Kong, W.: ‘Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform’, Infrared Phys. Technol., 2014, 63, pp. 110118.
    15. 15)
      • 15. Wang, Z., Wang, S., Zhu, Y., et al: ‘Review of image fusion based on pulse-coupled neural network’, Arch. Comput. Methods Eng., 2015, 23, (4), pp. 2629.
    16. 16)
      • 16. Lakhwani, K., Murarka, P.D., Chauhan, N.S.: ‘Color space transformation for visual enhancement of noisy color image’, Int. J. ICT Manag., 2015, 3, (2), pp. 911.
    17. 17)
      • 17. Ouerghi, H., Mourali, O., Zagrouba, E.: ‘Multimodal medical image fusion using modified PCNN based on linking strength estimation by MSVD transform’, Int. J. Comput. Commun. Eng., 2017, 6, (3), pp. 201211.
    18. 18)
      • 18. Das, S., Kundu, M.K.: ‘NSCT based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency’, Med. Biol. Eng., 2012, 50, (10), pp. 11051114.
    19. 19)
      • 19. Kong, W., Liu, J.: ‘Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network’, Opt. Eng., 2013, 52, (1), p. 017001.
    20. 20)
      • 20. Jin, X., Nie, R., Zhou, D., et al: ‘Multifocus color image fusion based on NSST and PCNN’, J. Sens., 2016, 2016.
    21. 21)
      • 21. Kong, W., Zhang, L., Lei, Y.: ‘Novel fusion method for visible light and infrared images based on NSST–SF–PCNN’, Infrared Phys. Technol., 2014, 65, pp. 103112.
    22. 22)
      • 22. Singh, S., Gupta, D., Anand, R.S., et al: ‘Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network’, Biomed. Signal Proc. Control, 2015, 18, pp. 91101.
    23. 23)
      • 23. Ganasala, P., Kumar, V.: ‘Feature-motivated simplified adaptive PCNN-based medical image fusion algorithm in NSST domain’, J. Digit. Imaging, 2015, 29, (1), pp. 7385.
    24. 24)
      • 24. Daneshvar, S., Ghassemian, H.: ‘MRI and PET image fusion by combining IHS and retina-inspired models’, Inf. Fusion, 2010, 11, (2), pp. 114123.
    25. 25)
      • 25. Ganasal, P., Kumar, V.: ‘Multimodality medical image fusion based on new features in NSST domain’, Biomed. Eng. Lett., 2014, 4, pp. 414424.
    26. 26)
      • 26. Jin, X., Zhou, D., Yao, S., et al: ‘Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks’, J. Appl. Remote Sens., 2016, 10, (2), p. 025023.
    27. 27)
      • 27. Bisht, S.S., Gupta, B., Rahi, P.: ‘Image registration concept and techniques: a review’, Int. J. Eng. Res. Appl., 2014, 4, (4), pp. 3035.
    28. 28)
      • 28. Alam, F., Rahman, S.U., Khalil, A., et al: ‘Deformable registration methods for medical images: a review based on performance comparison’, Proc. Pak. Acad. Sci., Phys. Comput. Sci., 2016, 53, (2), pp. 111130.
    29. 29)
      • 29. Heinrich, M.P., Jenkinson, M., Bhushan, M., et al: ‘MIND: modality independent neighbourhood descriptor for multi-modal deformable registration’, Med. Image Anal., 2012, 16, (7), pp. 14231435.
    30. 30)
      • 30. Das, S., Chowdhury, M., Kundu, M.K.: ‘Medical image fusion based on ripplet transform type-I’, Prog. Electromagn. Res. B., 2011, 30, pp. 355370.
    31. 31)
      • 31. Li, S., Kang, X., Hu, J.: ‘Image fusion with guided filtering’, IEEE Trans. Med. Imaging, 2013, 22, (7), pp. 28642875.
    32. 32)
      • 32. Jagalingam, P., Hegde, A.V.: ‘A review of quality metrics for fused image’. Int. Conf. on Water Resources, Coastal and Ocean Engineering, Aquatic Procedia, Karnataka, India, 2015, vol. 4, pp. 133142.
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