# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging  
This section discusses the implementation of SSIM, particularly for imaging techniques such as MRI, ultrasonography, CT scan, X-rays, and optical imaging. This section aims to emphasize reviews of SSIM applied to the measurement of medical images. We reviewed SSIM in medical imaging based on the published year of the articles. To identify the relevant studies, a systematic methods overview [80] along with several major databases were used to search the matched keywords, such as \"SSIM\" AND \"Magnetic Resonance Imaging\" OR \"Computed Tomography\" OR \"Ultrasonography\" OR \"Ultrasonod\" OR \"X-ray\" OR \"Optical Imaging\" OR \"Medical Images\" OR \"Medical Imaging\". The main databases included Google Scholar, PubMed, IEEE, MDPI, Springer, Elsevier, and others. There were 125 identified articles related to the keywords including journals, conference proceedings, and book chapters, consisting of the original articles on UQI, SSIM, MS-SSIM, three- and four-component weighted SSIMs, CW-SSIM, ISSIM-S, and other SSIM families, as well as the SSIM implementation for MRI, CT, ultrasonography, X-ray, and optical imaging. Overall, 72 articles relevant to the goal of this review paper regarding SSIM applied in medical imaging were reviewed. We did not exclude the same SSIM implementation in medical imaging as with an IQA, and instead, we mentioned, classified, and briefly reviewed them in Sections 4.1 and 4.5 according to the medical imaging technique used. We provided this method because we prefer to offer a wide range of SSIM implementations and fulfill the objective of this review paper of providing the readers or researchers with potential medical image examination research methods that can be improved using SSIM. Additionally, Section 4.6 provided a thorough review of 4 articles related to SSIM application in medical imaging for loss function in convolutional neural network (CNN), reducing metal artifact, contour extractor, and IQA. Thus, we described in detail the image acquisition method, filtering, or the other approaches used to acquire the distorted images for comparing with the original image from the medical modality imaging scheme. Additionally, this study's limitation was stated in Section 4.7. To avoid misleading and maintain the objective of this review, we emphasized that the SSIM measure has an original goal to substitute the common measures, such as MSE and PSNR in measuring any signals in 1D, 2D, and 3D, as long as there is a reference signal. Thus, the SSIM measure can assess digital images including medical images. To offer a better representation of the methodology when using SSIM to evaluate medical images, we provided Figures 13-15 to depict medical images when they were processed using three- and four-component SSIMs, as well as ISSIM-S. However, since this study's goal is to provide the SSIM prospect in medical images, we did not measure each SSIM type (three-component SSIM, four-component SSIM, and ISSIM-S) similarity score. We used a digital database for screening mammography (DDSM) [81] containing 2620 cases of normal, benign, and malignant breast cancers extracted from calcification and breast masses abnormalities. Figure 13 shows the images for every process in a specific region for three-component SSIM. Figure 13a depicts the original DDSM right breast masses benign cancer with craniocaudal (CC) view image, Figure 13b shows its blurred image, Figure 13c depicts the edge region image, Figure 13d shows the smooth region image, and Figure 13e depicts the texture region image. Figure 14 shows the images for every process within a specific region for four-component SSIM. Figure 14a depicts a preserved edge image of DDSM right breast masses benign cancer with CC, whereas Figure 14b shows a changed edge region image. In addition, Figure 14c displays a smooth region image, and Figure 14d depicts a texture region image.