# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Computed Tomography
According to [94], SSIM was able to quantify the improvement of fused images by showing the tumor location, whereas in [97], the SSIM aided in assessing the compressed medical images to determine the optimal block size for use in a compression method. SSIM showed a decreasing similarity score when the compressed block size was increased. This situation states that the SSIM can measure well the quality of the images according to the compression level. More compression makes more blurriness. With a certain blurriness, the medical images may not be easily readable, thus leading to the wrong diagnosis in telemedicine. The compression level is crucial because, with abundant compression, the important structural information of the images may be lost. By implementing the objective IQM, the threshold value of the compression level can be identified. Herein, we imply implicitly that SSIM has the potential to develop into the direction of a computer-aided diagnosis (CAD) method. In 2016, studies related to SSIM for CT images were identified for investigating the ability of a denoised method utilizing a Wiener filter and the threshold in the wavelet domain [62]. In addition, a two-dimensional (2D) filter to enhance the CT image resolution by maximizing the SSIM similarity value [98], a low-dose CT denoising algorithm using locally consistent non-local means (LC-NLM) [63], and fusion between CT and MR images implementing a principal component analysis (PCA) and the maximum selection [99] were developed. These four studies presented SSIM as a reliable quality measure to assess the improvement of the images. In 2017-2019, SSIM was found to be a quality metric for measuring the results from 3D printed lung vessels [100], an approach to reducing the metal artifact in CT images by excluding the luminance comparison [101], an IQM for deep learning [41; 43; 102], and an alternative random forest (ARF) regression tool [103]. It was also used for CT tooth images extracted from denoised images filtered using a wavelet and bilateral filter [65], the removal of Gaussian noise [42], and image restoration and reconstruction [64]. In [101], the role of SSIM was distinguished from IQA as a method to reduce the artifacts caused by metal. In addition, a modified SSIM was utilized to construct this task by ignoring the luminance factor but maintaining the contrast and structural comparisons. This modified SSIM should be completed because the metal artifacts and a superposition map may vary substantially, whereas the structural or edge information can be indistinguishable. With the role of SSIM, correlated images can be obtained, and two correlation maps can then be compared to acquire reduced metal artifact images. In 2020-2021, SSIM was used as an evaluation metric for ovarian cancer [104], a generative adversarial network (GAN) [105], and Franken-CT [67]. To conclude this subsection, Table 2 describes SSIM used for CT medical images. As indicated in Table 2, SSIM has been embraced not only for IQA but also for other objectives, such as noise reduction. In addition, compared to other metrics, SSIM performs competitively well according to the specialized task. In practical terms, all SSIM roles listed in Table 2 are for image quality measures. They indicate that SSIM has the potential to become a favorable IQA.