Skip to main content
Log in

Waveatom transform-based multimodal medical image fusion

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Medical image fusion produces a fused image that is extensively used by physicians for medical analysis and treatment. The fused image, so obtained, contains the complementary features present in different medical images obtained from imaging devices of single modality or of multiple modalities. The potential capabilities of waveatoms have been explored in many applications such as image denoising, fingerprint identification, compression; therefore, waveatom transform-based medical image fusion is proposed. The proposed fusion method is experimented on various sets of medical images and compared with recent state-of-the-art fusion methods. Results prove that the fused images obtained from the proposed method have better clarity and enhanced information and are practically more helpful for quick diagnosis and better treatment of diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bhatnagar, G., Jonathan Wu, Q.M., Zheng, L.: A new contrast based multimodal medical image fusion framework. Neurocomputing 157, 143–152 (2015)

    Article  Google Scholar 

  2. Demanet, L., Ying, L.: Wave atoms and sparsity of oscillatory patterns. Appl. Comput. Harmonic Anal. 23(3), 368–387 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ding, K.: Wavelets, curvelets and wave atoms for image denoising. In: 3rd IEEE International Congress on Image and Signal Processing (CISP), vol. 2, pp. 782–786 (2010)

  4. Du, C., Gao, S.: Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network. IEEE Access. 5, 15750–15761 (2017)

    Article  Google Scholar 

  5. Ghantous, M., Bayoumi, M.: MIRF: a multimodal image registration and fusion module based on DT-CWT. J. Signal Process. Syst. 71(1), 41–55 (2013)

    Article  Google Scholar 

  6. Haddad, Z., Beghdadi, A., Serir, A., Mokraoui, A.: Wave atoms based compression method for fingerprint images. Pattern Recognit. 46(9), 2450–2464 (2013)

    Article  Google Scholar 

  7. Xueke, L., Xiaolin, T., Yankui, S., Zesheng, T.: Medical image fusion by multi-resolution analysis of wavelets transform. In: Wavelet Analysis and Applications, pp. 389–396. Springer (2006)

  8. Liu, Y., Chen, X., Cheng, J., Peng, H.: A medical image fusion method based on convolutional neural networks. In: 20th International Conference on Information Fusion, pp. 1–7. IEEE (2017)

  9. Liu, Y., Chen, X., Peng, H., Wang, Z.: Multi-focus image fusion with a deep convolutional neural network. Inf. Fusion 36, 191–207 (2017)

    Article  Google Scholar 

  10. Liu, Y., Chen, X., Wang, Z., Wang, Z.J., Ward, R.K., Wang, X.: Deep learning for pixel-level image fusion: recent advances and future prospects. Inf. Fusion 42, 158–173 (2018)

    Article  Google Scholar 

  11. Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 24, 147–164 (2015)

    Article  Google Scholar 

  12. Ma, J., Zhao, J., Ma, Y., Tian, J.: Non-rigid visible and infrared face registration via regularized gaussian fields criterion. Pattern Recognit. 48(3), 772–784 (2015)

    Article  Google Scholar 

  13. Manchanda, M., Sharma, R.: A novel method of multimodal medical image fusion using fuzzy transform. J. Vis. Commun. Image Represent. 40, 197–217 (2016)

    Article  Google Scholar 

  14. Manchanda, M., Sharma, R.: An improved multimodal medical image fusion algorithm based on fuzzy transform. J. Vis. Commun. Image Represent. 51, 76–94 (2018)

    Article  Google Scholar 

  15. Singh, R., Khare, A.: Fusion of multimodal medical images using Daubechies complex wavelet transform—a multiresolution approach. Inf. Fusion 19, 49–60 (2014)

    Article  Google Scholar 

  16. Villemoes, L.F.: Wavelet packets with uniform time-frequency localization. Comptes Rendus Mathematique 335(10), 793–796 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  17. Yong, Y., Dongsun, P., Shuying, H., Nini, R.: Medical image fusion via an effective wavelet-based approach. EURASIP J. Adv. Signal Process. 2010(1), 579341 (2010)

    Article  Google Scholar 

  18. Zhang, Q., Guo, B.L.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process. 89(7), 1334–1346 (2009)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meenu Manchanda.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gambhir, D., Manchanda, M. Waveatom transform-based multimodal medical image fusion. SIViP 13, 321–329 (2019). https://doi.org/10.1007/s11760-018-1360-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1360-3

Keywords

Navigation