Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images.
Image quality assessments (IQA) are an important task for providing appropriate medical care. Full-reference IQA (FR-IQA) methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are often used to evaluate imaging conditions, reconstruction conditions, and image processin...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2623 - 2635 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173050933&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050933 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050933 169804341 173050933 173050933 10.1007/s10278-023-00875-0 173050933 ppf: 2623 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images. aug: au: Ohashi, Kohei Nagatani, Yukihiro Yoshigoe, Makoto Iwai, Kyohei Tsuchiya, Keiko Hino, Atsunobu Kida, Yukako Yamazaki, Asumi Ishida, Takayuki affil: Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Japan sug: subj: Image Enhancement Evaluation Quality Assessment Methods Tomography, X-Ray Computed Human Funding Source Sensitivity and Specificity Spearman's Rank Correlation Coefficient ab: Image quality assessments (IQA) are an important task for providing appropriate medical care. Full-reference IQA (FR-IQA) methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are often used to evaluate imaging conditions, reconstruction conditions, and image processing algorithms, including noise reduction and super-resolution technology. However, these IQA methods may be inapplicable for medical images because they were designed for natural images. Therefore, this study aimed to investigate the correlation between objective assessment by some FR-IQA methods and human subjective assessment for computed tomography (CT) images. For evaluation, 210 distorted images were created from six original images using two types of degradation: noise and blur. We employed nine widely used FR-IQA methods for natural images: PSNR, SSIM, feature similarity (FSIM), information fidelity criterion (IFC), visual information fidelity (VIF), noise quality measure (NQM), visual signal-to-noise ratio (VSNR), multi-scale SSIM (MSSSIM), and information content-weighted SSIM (IWSSIM). Six observers performed subjective assessments using the double stimulus continuous quality scale (DSCQS) method. The performance of IQA methods was quantified using Pearson's linear correlation coefficient (PLCC), Spearman rank order correlation coefficient (SROCC), and root-mean-square error (RMSE). Nine FR-IQA methods developed for natural images were all strongly correlated with the subjective assessment (PLCC and SROCC > 0.8), indicating that these methods can apply to CT images. Particularly, VIF had the best values for all three items, PLCC, SROCC, and RMSE. These results suggest that VIF provides the most accurate alternative measure to subjective assessments for CT images. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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