# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Computed Tomography  
To the best of our knowledge, SSIM was used for the first time to evaluate CT images in 2007 and was assigned as the IQM for image fusion between CT and MR images [96], \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Modality** & \begin{tabular}{c} **SSIM** \\ **Implementation** \\ \end{tabular} & \begin{tabular}{c} **Compared** \\ **Matrix** \\ \end{tabular} & **Results** \\ \hline [PERSON] et al. [83] & 2005 & MRI & IQA & \begin{tabular}{c} MAE, RMSE, SNR, \\ and PSNR \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [82] & 2005 & MRI & IQA & Subjective measure & The subjective measure \\  & & & & & is superior \\ \hline [PERSON] et al. [31] & 2007 & MRI and CT & IQA & PSNR and SVDP & \begin{tabular}{c} SVDP is suitable for \\ this research \\ \end{tabular} & \begin{tabular}{c} SVDP is suitable for \\ this research \\ \end{tabular} \\ \hline [PERSON] et al. [84] & 2007 & MRI & IQA & MSE, QILV, and LN & \begin{tabular}{c} SSIM has competitive results \\ compared to QILV \\ \end{tabular} \\ \hline [PERSON] et al. [85] & 2009 & MRI and CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [86] & 2009 & MRI and CT & Images fusion & \begin{tabular}{c} LP, GP, CP, Str, \\ and DWI \\ \end{tabular} & \(Q^{AB/F}=0.62\) \\ \hline [PERSON] et al. [87] & 2012 & MRI & IQA & MSE and MOS & SSIM is suitable with MOS \\ \hline [PERSON] et al. [37] & 2013 & MRI & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [88] & 2015 & MRI & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [89] & 2015 & MRI & IQA & \begin{tabular}{c} PSNR, \\ MSE, RMSE \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [40] & 2017 & MRI & IQA & \begin{tabular}{c} SNR, PSNR, \\ MSE, RMSE \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [90] & 2017 & MRI & IQA & \begin{tabular}{c} PSNR, entropy, \\ and MSE \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] et al. [92] & 2017 & MRI & Segmentation & SI & Accuracy = 97.5\% \\ \hline [PERSON] et al. [39] & 2019 & MRI & IQA & MAE & SSIM is suitable for this task \\ \hline [PERSON] et al. [93] & 2019 & MRI & IQA & NRMSE & SSIM is suitable for this task \\ \hline [PERSON] et al. [95] & 2019 & MRI & Clustering & - & \(SSIM=0.7\) to \(>0.9\) \\ \hline [PERSON] et al. [94] & 2020 & MRI & IQA & PSNR and NRMSE & \begin{tabular}{c} SSIM and NRMSE \\ performances are decent \\ \end{tabular} \\ \hline [PERSON] et al. [36] & 2020 & MRI & IQA & \
\begin{tabular}{c} MOS, VIF, FSIM, \\ NOM, GMSD, \\ HRVDP, PSNR, \\ and RMSE \\ \end{tabular} & VIF shows the decent results \\ \hline [PERSON] and [PERSON] [91] & 2020 & MRI & IQA &
\begin{tabular}{c} PSNR, MSE, \\ and entropy \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] et al. [38] * & 2021 & MRI & Loss function & MAE & \(p\) value \(<0.05\) \\ \hline \hline \end{tabular}  
* A detailed review is provided in Section 4.6.  
\end{table} 
Table 1: Summary of published articles encompassing SSIM for MRI. MRI, magnetic resonance imaging; CT, computed tomography; MAE, mean-absolute error; RMSE, root-mean-square error; SNR, signal-to-noise ratio; PSNR, peak-signal-to-noise ratio; SVDP, steerable visual difference predictor; QILV, quality index based on local variance; LN, level on noise; LP, Laplacian pyramid; GP, gradient pyramid; CP, contrast pyramid; StrP, steerable pyramid; DWT, discrete wavelet transform; \(Q^{AB/F}\), visual information quality; MOS, mean opinion score; SI, similarity index; NRMSE, normalized-root-mean-square error; VIF, visual information fidelity; FSIM, feature similarity index; NQM, noise quality metric; GMSD, gradient magnitude similarity deviation; HDRVDP, high dynamic range visible difference predictor; CNN, convolutional neural network. as well as for CT, US, and X-ray medical image compression using DCT [97].