ArticleJournal of medical imaging (Bellingham, Wash.)2017
Structural similarity index family for image quality assessment in radiological images.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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58 citing papers in PubMed, 1 synthesis or guideline pooled it.
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The structural similarity index (SSIM) family is a set of metrics that has demonstrated good agreement with human observers in tasks using reference images. These metrics analyze the viewing distance, edge information between the reference and the test images, changed and preserved edges, textures, and structural similarity of the images. Eight metrics based on that family are proposed. This new set of metrics, together with another eight well-known SSIM family metrics, was tested to predict human performance in some specific tasks closely related to the evaluation of radiological medical images. We used a database of radiological images, comprising different acquisition techniques (MRI and plain films). This database was distorted with different types of distortions (Gaussian blur, noise, etc.) and different levels of degradation. These images were analyzed by a board of radiologists with a double-stimulus methodology, and their results were compared with those obtained from the 16 metrics analyzed and proposed in this research. Our experimental results showed that the readings of human observers were sensitive to the changes and preservation of the edge information between the reference and test images, changes and preservation in the texture, structural component of the images, and simulation of multiple viewing distances. These results showed that several metrics that apply this multifactorial approach (4-G-SSIM, 4-MS-G-SSIM, [Formula: see text], and [Formula: see text]) can be used as good surrogates of a radiologist to analyze the medical quality of an image in an environment with a reference image.
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