Skip to main content
SpringerLink
Log in

Medical image fusion using non subsampled contourlet transform and iterative joint filter

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This study proposes an improved medical image fusion scheme based on components of non subsampled contourlet transform (NSCT) and iterative joint filter. Multimodal images are split into approximation and detail components using NSCT. The former are subsequently normalized and further smoothed using box filter. The underlying morphological structure of the smoothened components is obtained with the help of gradient operator using Wiener filter. The filtered structures are then used to compute decision map. Iterative joint filter is finally applied on the decision map along with input guidance image to compute the resultant image. Eight performance metrics as well as qualitative visual evaluation shows the efficacy of the proposed fusion scheme.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Barra V, Boire JY (2001) A general framework for the fusion of anatomical and functional medical images. NeuroImage 13(3):410–424

    Article  Google Scholar 

  2. Bavirisetti DP, Kollu V, Gang X, Dhuli R (2017) Fusion of MRI and CT images using guided image filter and image statistics. Int J Imaging Syst Technol 27(3):227–237

    Article  Google Scholar 

  3. Bhatnagar G, Wu QJ, Liu Z (2013) Human visual system inspired multi-modal medical image fusion framework. Expert Syst Appl 40(5):1708–1720

    Article  Google Scholar 

  4. Ch MMI, Riaz MM, Iltaf N, Ghafoor A, Sadiq MA (2019) Magnetic resonance and computed tomography image fusion using saliency map and cross bilateral filter. Signal, Image and Video Processing, pp 1–8

  5. Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. In: ACM transactions on graphics (TOG), vol 26, No 3. ACM, p 51

  6. Gambhir D, Manchanda M (2019) Waveatom transform-based multimodal medical image fusion. SIViP 13(2):321–329

    Article  Google Scholar 

  7. Haghighat M, Razian MA (2014) Fast-FMI: non-reference image fusion metric. In: 2014 IEEE 8th international conference on application of information and communication technologies (AICT). IEEE, pp 1–3

  8. Harvard Image Database. Available: https://www.med.harvard.edu. Accessed 29 Nov 2017

  9. Hill PR, Canagarajah CN, Bull DR (2002) Image fusion using complex wavelets. In: BMVC, pp 1–10

  10. Jose J, Gautam N, Tiwari M, Tiwari T, Suresh A, Sundararaj V, Rejeesh MR (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control 66:102480

    Article  Google Scholar 

  11. Kong W, Chen Y, Lei Y (2021) Medical image fusion using guided filter random walks and spatial frequency in framelet domain. Signal Processing 181:107921

    Article  Google Scholar 

  12. Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell (2) 165–177

  13. Kumar BS (2015) Image fusion based on pixel significance using cross bilateral filter. Signal Image Video Processing 9(5):1193–1204

    Article  Google Scholar 

  14. Langari B, Vaseghi S, Prochazka A, Vaziri B, Aria FT (2016) Edge-guided image gap interpolation using multi-scale transformation. IEEE Trans Image Process 25(9):4394–4405

    Article  MathSciNet  Google Scholar 

  15. Leng L, Zhang J (2013) Palmhash code vs. palmphasor code. Neurocomputing 108:1–2

    Article  Google Scholar 

  16. Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. International Journal of Physical Sciences 5(17):2543–2554

    Google Scholar 

  17. Leng L, Li M, Teoh AB (2013) Conjugate 2D PalmHash code for secure palm-print-vein verification. In: 2013 6th International congress on image and signal processing (CISP), vol 3. IEEE, pp 1705–1710

  18. Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimedia Tools and Applications 76(1):333–354

    Article  Google Scholar 

  19. Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Transactions on Image Processing 22(7):2864–2875

    Article  Google Scholar 

  20. Li W, Xie Y, Zhou H, Han Y, Zhan K (2018) Structure-aware image fusion. Optik 172:1–1

    Article  Google Scholar 

  21. Liu Y, Liu S, Wang Z (2014) Medical image fusion by combining nonsubsampled contourlet transform and sparse representation. In: Chinese conference on pattern recognition 2014. Springer, Berlin, pp 372–381

  22. Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Inform Fusion 24:147–64

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607

    Article  Google Scholar 

  25. Piella G, Heijmans H (2003) A new quality metric for image fusion. In: Proceedings 2003 international conference on image processing (Cat. No. 03CH37429) vol 3. IEEE, pp III–173

  26. Polinati S, Dhuli R (2020) Multimodal medical image fusion using empirical wavelet decomposition and local energy maxima. Optik 205:163947

    Article  Google Scholar 

  27. Rajasekhar G, Prasad VV, Srikanth MV (2017) Multilevel medical image fusion using multi-level local extrema and non sub-sampled contourlet transformation. In: 2017 international conference on signal processing and communication (ICSPC). IEEE, pp 246–252

  28. Shah P, Srikanth TV, Merchant SN, Desai UB (2014) Multimodal image/video fusion rule using generalized pixel significance based on statistical properties of the neighborhood. SIViP 8(4):723–738

    Article  Google Scholar 

  29. Shahdoosti HR, Mehrabi A (2018) MRI And PET image fusion using structure tensor and dual ripplet-II transform. Multimedia Tools and Applications 77(17):22649–22670

    Article  Google Scholar 

  30. Shen R, Cheng I, Basu A (2013) Cross-scale coefficient selection for volumetric medical image fusion. IEEE Trans Biomed Eng 60(4):1069–1079

    Article  Google Scholar 

  31. Tawfik N, Elnemr HA, Fakhr M, Dessouky MI, Abd El-Samie FE (2020) Survey study of multimodality medical image fusion methods. Multimedia Tools and Applications. 15:1–28

    Google Scholar 

  32. Tuba E, Strumberger I, Bacanin N, Zivkovic D, Tuba M (2019) Brain Storm Optimization Algorithm for Thermal Image Fusion using DCT Coefficients. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE, pp 234–241

  33. Xu Z (2014) Medical image fusion using multi-level local extrema. Inform Fusion 19:38–48

    Article  Google Scholar 

  34. Yang C, Zhang JQ, Wang XR, Liu X (2008) A novel similarity based quality metric for image fusion. Inform Fusion 9(2):156–160

    Article  Google Scholar 

  35. Yin M, Liu X, Liu Y, Chen X (2018) Medical image fusion with Parameter-Adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Transactions on Instrumentation and Measurement (99):1–16

Download references

Author information

Authors and Affiliations

  1. Institute of Information Technology, Quaid-i-Azam University, Islamabad, Pakistan

    M Munawwar Iqbal Ch

  2. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Abdul Ghafoor & Asim Dilawar Bakhshi

  3. Foundation University Medical College and Fauji Foundation Hospital, Rawalpindi, Pakistan

    Nuwayrah Jawaid Saghir

Authors
  1. M Munawwar Iqbal Ch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Ghafoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asim Dilawar Bakhshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nuwayrah Jawaid Saghir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M Munawwar Iqbal Ch.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ch, M.M.I., Ghafoor, A., Bakhshi, A.D. et al. Medical image fusion using non subsampled contourlet transform and iterative joint filter. Multimed Tools Appl 81, 4495–4509 (2022). https://doi.org/10.1007/s11042-021-11753-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11753-8

Keywords

Search

Navigation