# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Optical Imaging  
To complete the reviews of SSIM implementation in medical imaging systems, we presented a novel modality using optical imaging. Because optical imaging is a relatively new modality, acquiring SSIM applications in optical imaging is considered rare. Despite our best efforts, we found only two articles comprising SSIM as an IQM to measure the distorted medical images related to the reference images. We excluded optical imaging using  
\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. [120] & 2012 & X-ray & IQA & \begin{tabular}{c} PSNR, SNR, and \\ MSE \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] et al. [121] & 2016 & X-ray & IQA & \begin{tabular}{c} MSE, RMSE, SNR, \\ PSNR, recall, \\ accuracy, precision, \\ and error rate \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] [122] & 2016 & X-Ray & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [123] & 2017 & Xray and MRI & IQA & PSNR and WSNR & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [124] & 2018 & X-ray CT & IQA & RMSE & SSIM is suitable for this task \\ \hline [PERSON] et al. [125] & 2019 & X-ray & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [126] & 2019 & X-ray & IQA & PSNR & SSIM is suitable for this task \\ \hline [PERSON] et al. [127] & 2020 & X-ray & IQA & RMSE and PSNR & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [128] & 2020 & X-ray & IQA & \
\begin{tabular}{c} MSE, PSNR, \\ and SNR \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] et al. [129] & 2020 & X-ray & IQA &
\begin{tabular}{c} RMSE, PSNR, \\ and FSIM \\ \end{tabular} & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [130] & 2020 & X-ray CT & IQA & RMSE and PSNR & SSIM is suitable for this task \\ \hline [PERSON] and [PERSON] [44]* & 2021 & X-ray & IQA & PSNR and MSE & SSIM is suitable for this task \\ \hline \hline \end{tabular}  
* A detailed review is provided in Section 4.6.  
\end{table}
Table 4: Summary of the published articles encompassing SSIM for X-ray. PSNR, peak-signal-to-noise ratio; CT, computed tomography; MRI, magnetic resonance imaging; SNR, signal-to-noise ratio; MSE, mean-square error; RMSE, root-mean-square error; WSN, wavelet domain signal-to-noise ratio; RMSE, root-mean-square error; FSIM, feature similarity index.  
SSIM for a scope other than the medical field, such as optical imaging for communication and simulations to predict the optical properties of a reconstruction. In 2020, SSIM along with MAE were utilized to examine images from a single snapshot of optical properties (SSOP) and the ground-truth images [131].  
In 2021, SSIM was used for comparing the optical images of a mouse with an embedded tumor [132]. The article reported the application of machine learning, such as K-means and fuzzy c-means, to automatically cluster the tumor. They compared the results between manual clustering and automatic clustering with the aid of SSIM and the Dice coefficient to evaluate the similarity of two images. Here, SSIM performed satisfactorily in objectively examining the segmentation results through a manual approach, showing a similarity score of 0.9168. In addition, when Gaussian noise was added to the background, SSIM demonstrated a robust performance by showing a stable similarity score. With the assistance of SSIM, the article reported that their clustering algorithms are robust to noise.  
To conclude this subsection, Table 5 represents the purposes of using SSIM for optical medical images. As in Table 5, we describe two articles using optical images assigning the SSIM metric as an IQM.