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This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index. Additionally, potential research directions in applying such similarities related to medical image analyses are described. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. 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 medical imaging applications have been rare. Structural similarity has since been widely used in various sectors, including medical image evaluation. 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. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points.Īn image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. Image texture feature extraction technology is implemented.

On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method.

For this reason, an image texture feature extraction technology based on the digital twin is proposed. The anti-interference performance is weak. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features.

The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis.