# Prospects of Structural Similarity Index for Medical Image Analysis
###### Abstract  
An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for  
ratio (SNR), contrast-to-noise ratio (CNR), and absolute error (AE), as well as a derivation of such measures, such as Laplacian MSE (LMSE), peak MSE (PMSE), normalized MSE (NMSE), PSNR, and NAE [6,7]. Although such measures are accepted as a universal image quality index, they provide less sensitivity compared with a human visual system (HVS) [8]. They are considered common measurements because they can be calculated easily and may interpret the physical meaning of an image [9,10]. In addition, contrast-and-size detail (CSD) was developed to separate the visible and invisible inclusions in mimicking the breast environment through simulations by embedding a numerical tumor. In this case, an inclusion represents a breast tumor [11,12]. Again, this image quality metric (IQM) has issues in terms of inconsistency when increasing the contrast ratio with a raised inclusion size and an ambiguous threshold value. Therefore, it is challenging to implement this method and distinguish seeable and unseeable inclusions inside the breast tissue with a provided threshold value. In early 2000, [PERSON] et al. developed a new universal IQA and tried to replace conventional methods such as MSE and PSNR for measuring the quality of the images, namely the structural similarity (SSIM) index, and adapt to the HVS [10,13]. Their first attempt regarding the possibility of substituting traditional strategies with an applicable metric to measure various images from numerous sectors was reported in 2002. It was reported that the IQA calculates the distortions in a combination of the loss of correlation, luminance distortion, and contrast distortion [14]. In addition, their results indicate that the novel universal index is more exceptional than MSE because the new index measures the information loss and is not focused on the energy loss. This is reasonable because the MSE values of two different distorted images can be the same, although one image is more flawless than another image. Their follow-up study complementing their previous research was published in 2004, and one of the most popular IQMs in this era, i.e., SSIM, was described [10]. They proposed a novel philosophy by considering that image degradation is the perceived changes in structural information, whereas error sensitivity is an estimation of the perceived errors to assess a noised image in comparison with the original. This new philosophy is easy to understand because the human perceptual measure is comfortable quantifying the changes in structural information when two images are compared, and it is more complicated to indicate the error. Moreover, the novel metric suggests the IQA by considering three factors, i.e., luminance, contrast, and structure comparisons. In addition, they suggested that SSIM may be used for several applications [15] other than image processing because SSIM quantifies two signals and compares them to obtain the similarity score, regardless of the complexity in calculating the SSIM when compared to that of the MSE.