# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 4 Conclusions and Future Scope  
We proposed a multi-modal medical image fusion framework with VMD and LEM to fuse MRI and CT medical images in this work. By using an adaptive decomposition technique VMD, significant IMFs are derived from the source images. This decomposition process can preserve some details of source images. However, these details are not sufficient to fulfill the clinical needs of radiologists. Hence, we used a LEM fusion rule to preserve complementary information from IMFs, an essential criterion during medical image diagnosis. All the experiments are evaluated on the Whole Brain Atlas benchmark data sets to analyze the efficacy of the proposed methodology. The experimental results reveal that the proposed framework attained better visual perception. Even objective assessment in terms of average EI (64.582), MI (3.830), VIFF (0.498), \(Q_{p}^{AB/F}\) (0.542), SSIM (0.6574), RMSE (0.020), AG (6.41), and PSNR (20.291) demonstrated quantitative fusion performance better than the existing multi-modal fusion approaches. In the future, we wish to conduct experiments with extensive data that contain images of MRI and CT with different disease information. Additionally, we consider extending this work to both 2D and 3D image clinical applications. Furthermore, we would like to verify the effectiveness of the proposed method for other image fusion applications such as digital photography, remote sensing, battlefield monitoring, and military. The following are available online at [[https://www.mdpi.com/article/10.3390/app112210975/s1](https://www.mdpi.com/article/10.3390/app112210975/s1)]([https://www.mdpi.com/article/10.3390/app112210975/s1](https://www.mdpi.com/article/10.3390/app112210975/s1)). The qualitative (Figures S1-S23) and quantitative results (Tables S1-S23) of all the 23 pairs of images used in this work are given in the supplementary material. Conceptualization, S.P. and R.D. ; methodology, S.P., D.P.B., R.D., K.N.V.P.S.R. and G.R.N. ; software, S.P., D.P.B. and K.N.V.P.S.R. ; validation, S.P., D.P.B. and K.N.V.P.S.R. ; formal analysis, S.P. and G.R.N. ; investigation, K.N.V.P.S.R. and G.R.N. ; resources, S.P. and R.D. ; data curation, K.N.V.P.S.R. and D.P.B. ; writing--original draft preparation, S.P., D.P.B. and K.N.V.P.S.R. ; writing--review and editing, S.P., D.P.B. and K.N.V.P.S.R. ; visualization, S.P., R.D. and G.R.N. ; supervision, R.D. and G.R.N. ; project administration, K.N.V.P.S.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. M.A.A.A.A.A.A.A.A.A.A.A.A. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images. _Entropy_**2015**, _17_, 3461-3478. [CrossRef]
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