ArticleEntropy (Basel, Switzerland)2020
Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Image Quality Assessment Methods in Clinical Diagnostic Imaging: Overview of Current Technical Perspectives.Diagnostics (Basel, Switzerland) · 2026Article
- A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images.Journal of imaging informatics in medicine · 2025Review
- An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images.BMC medical imaging · 2024Article
- How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale.European radiology · 2024Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.Journal of imaging · 2022Article
- Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.Sensors (Basel, Switzerland) · 2021Article
- Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing.Sensors (Basel, Switzerland) · 2020Article
- Entropy in Image Analysis II.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
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Abstract
An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.
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Registered trials
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