# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Ultrasonography
\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 \begin{tabular}{c} [PERSON] and [PERSON] \\ [96] \\ \end{tabular} & 2007 & CT and MRI & IQA & RMSE & SSIM is suitable for this task \\ \hline \begin{tabular}{c} [PERSON] et al. [97] \\ \end{tabular} & 2007 & \begin{tabular}{c} CT, US, \\ and X-ray \\ \end{tabular} & IQA & \begin{tabular}{c} MSE, PSNR, PRD, \\ and CC \\ \end{tabular} & SSIM has the highest score \\ \hline \begin{tabular}{c} [PERSON] and [PERSON] [62] \\ \end{tabular} & 2016 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline \begin{tabular}{c} [PERSON] et al. [98] \\ \end{tabular} & 2016 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] [63] & 2016 & CT & IQA & - & SSIM is suitable for this task \\ \hline [PERSON] et al. [99] & 2016 & CT and MRI & IQA & FF & SSIM is suitable for this task \\ \hline \begin{tabular}{c} [PERSON] and [PERSON] [100] \\ \end{tabular} & 2017 & CT & IQA & - & SSIM is suitable for this task \\ \hline [PERSON] et al. [102] & 2018 & CT & IQA & RMSE & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [43] & 2018 & CT & IQA & - & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [103] & 2018 & CT and MRI & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [65] & 2018 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [41] & 2019 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [99]\({}^{\star}\) & 2019 & CT & \begin{tabular}{c} Reducing \\ metal artifact \\ \end{tabular} & \
\begin{tabular}{c} LI-MAR, NMAR, \\ and RMAR \\ \end{tabular} & \(MAE=8.52\) and \(10.12\) \\ \hline [PERSON] et al. [42] & 2019 & CT and MRI & IQA & PNSR, IQI, and VIF & SSIM is suitable for this task \\ \hline [PERSON] et al. [64] & 2019 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [104] & 2020 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [105] & 2021 & CT & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [67] & 2021 & CT and MRI & IQA &
\begin{tabular}{c} ZNCC, MAE, and \\ Dice coefficient for \\ bone class \\ \end{tabular} & SSIM is suitable for this task \\ \hline \hline \end{tabular} \({}^{\star}\) A detailed review is provided in Section 4.6.  
\end{table} 
Table 2: Summary of the published articles encompassing SSIM for CT. CT, computed tomography; MRI, magnetic resonance imaging; RMSE, root-mean-square error; MSE, mean-square error; PSNR, peak-signal-to-noise ratio; PRD, percent rate of distortion; CC, correlation coefficient; FF, fusion factor; LI-MAR, linear interpolation metal artifact reduction; NMAR, normalized metal artifact reduction; RMAR, refined metal artifact reduction; IQI, image quality index; VIF, visual information fidelity; ZNCC, zero-normalized cross-correlation; MAE, mean-absolute error. MSSIM and the NPSNR demonstrated similar results, whereas CW-SSIM presented superior results by showing the tongue position with the peak of CW-SSIM. Apparently, more attention from researchers was drawn in 2017-2021 for creating an efficient algorithm to eliminate speckle in US images [58; 59; 66; 112; 113; 114; 115; 116; 117; 118]. In addition, US images can be analyzed using deep learning to conduct breast tumor segmentation and reconstruct images from raw channel data [53; 119]. In [53], the combination between SSIM and L1-norm in the loss function was applied to capture the local context information from the surrounding tumor area, whereas in [119], MS-SSIM and PSNR were utilized as the loss function. To conclude this subsection, Table 3 illustrates the purposes of using SSIM for US medical images. As indicated in Table 3, SSIM is accepted not only for IQA but also for other purposes, such as a contour extractor and loss function.