Medical image fusion is the process of integrating two medical images with a visual enhanced single fused image, to attain a resultant image richer in information to aid medical practitioners in better diagnosis. A perceptual medical image fusion method is proposed by employing Internal Generative Mechanism. First, source images are divided into a predicted layer and a detail layer with a Bayesian prediction model. Then, the detail layer is merged with the energy of Tchebichef moments for blocks while the predicted layer is fused using the averaging strategy as activity level measurement. The fused image is finally obtained by merging coefficients in both fused layers. Experimental results prove that the proposed fusion algorithm is superior to the previously developed methods.

References

1. 1)
  - 10. Gu, K., Zhai, G., Yang, X., et al: ‘Hybrid no-reference quality metric for singly and multiply distorted images’, Trans. Broadcast., 2014, 60, (3), pp. 555–567 (doi: 10.1109/TBC.2014.2344471).
2. 2)
  - 1. Paulino, A. C., Thorstad, W. L., Fox, T.: ‘Role of fusion in radiotherapy treatment planning’, Semin. Nucl. Med., 2003, 33, (3), pp. 238–243 (doi: 10.1053/snuc.2003.127313).
3. 3)
  - 16. Qu, G., Zhang, D., Yan, P.: ‘Information measure for performance of image fusion’, Electron. Lett., 2002, 38, (7), pp. 313–315 (doi: 10.1049/el:20020212).
4. 4)
  - 18. Yang, C., Zhang, J., Wang, X., et al: ‘A novel similarity based quality metric for image fusion’, Inf. Fusion., 2008, 9, (2), pp. 156–160 (doi: 10.1016/j.inffus.2006.09.001).
5. 5)
  - 8. Gu, K., Zhai, G., Lin, W., Yang, X., Zhang, W.: ‘Learning a blind quality evaluation engine of screen content images’, Neurocomputing, 2016, 196, pp. 140–149 (doi: 10.1016/j.neucom.2015.11.101).
6. 6)
  - 2. Yang, Y., Song, T., Huang, S., et al: ‘Log-Gabor energy based multimodal medical image fusion in NSCT domain’, Comput. Math. Methods Med., 2014, 2014, (2), pp. 1–12.
7. 7)
  - 8. Gu, K., Zhai, G., Lin, W., et al: ‘Visual saliency detection with free energy theory’, Signal Process. Lett., 2015, 22, (10), pp. 1552–1555 (doi: 10.1109/LSP.2015.2413944).
8. 8)
  - 5. Bhatnagar, G., Wu, Q., Liu, Z.: ‘A new contrast based multimodal medical image fusion framework’, Neurocomputing, 2015, 2015, (157), pp. 143–152 (doi: 10.1016/j.neucom.2015.01.025).
9. 9)
  - 6. Liu, Y., Chen, X., Ward, R. K., et al: ‘Image fusion with convolutional sparse representation’, Signal Process. Lett., 2016, 23, (12), pp. 1882–1886 (doi: 10.1109/LSP.2016.2618776).
10. 10)
  - 13. Li, L., Lin, W., Wang, X., et al: ‘No-reference image blur assessment based on discrete orthogonal moments’, Trans. Cybern., 2016, 46, (1), pp. 39–50 (doi: 10.1109/TCYB.2015.2392129).
11. 11)
  - 4. Liu, Y., Liu, S., Wang, Z.: ‘A general framework for image fusion based on multi-scale transform and sparse representation’, Inf. Fusion, 2015, 24, pp. 147–164 (doi: 10.1016/j.inffus.2014.09.004).
12. 12)
  - 6. Xydeas, C., Petrović, V.: ‘Objective image fusion performance measure’, Electron. Lett., 2000, 36, (4), pp. 308–309 (doi: 10.1049/el:20000267).
13. 13)
  - 7. Knill, D. C., Pouget, A.: ‘The Bayesian brain: the role of uncertainty in neural coding and computation’, Trends Neurosci., 2004, 27, (12), pp. 712–719 (doi: 10.1016/j.tins.2004.10.007).
14. 14)
  - 3. Das, S., Kundu, M.: ‘NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency’, Med. Biol. Eng. Comput., 2012, 50, (10), pp. 1105–1114 (doi: 10.1007/s11517-012-0943-3).
15. 15)
  - 12. Gu, K., Zhai, G., Lin, W., et al: ‘No-reference image sharpness assessment in autoregressive parameter space’, Trans. Image Process., 2015, 24, (10), pp. 3218–3231 (doi: 10.1109/TIP.2015.2439035).
16. 16)
  - 14. Li, S., Kang, X., Hu, J.: ‘Image fusion with guided filtering’, Trans. Image Process., 2013, 22, (7), pp. 2864–2875 (doi: 10.1109/TIP.2013.2253483).
17. 17)
  - 7. Gu, K., Zhai, G., Yang, X., Zhang, W.: ‘Using free energy principle for blind image quality assessment’, IEEE Trans. Multimedia, 2015, 17, pp. 50–63 (doi: 10.1109/TMM.2014.2373812).
18. 18)
  - 15. Liu, S., Zhang, T., Li, H., et al: ‘Medical image fusion based on nuclear norm minimization’, Int. J. Imaging Syst. Technol., 2015, 25, (4), pp. 310–316 (doi: 10.1002/ima.22145).

Perceptual medical image fusion with internal generative mechanism

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