Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image Quality Assessment

Zhang, Zhicheng; Dai, Guangzhe; Liang, Xiaokun; Yu, Shaode; Li, Leida; Xie, Yaoqin

doi:10.1109/access.2018.2796632

Cited by 39 publications

(25 citation statements)

References 69 publications

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“…The ENMIQA is compared against the following ten related BIQA measures: SNRTOI [ 18 ], BPRI [ 29 ], ILNIQE [ 30 ], QENI [ 31 ], SISBLIM [ 32 ], metricQ [ 33 ], SSEQ [ 34 ], SINDEX [ 35 ], MEON [ 36 ], and DEEPIQ [ 37 ]. The SNRTOI [ 18 ] was implemented by authors of this paper, while other methods were run using their publicly available Matlab implementations. All compared methods, similarly to ENMIQA, do not require training.…”

Section: Resultsmentioning

confidence: 99%

“…In the work of Yu et al [ 16 ], four BIQA methods, i.e., BRISQUE, Natural Image Evaluator (NIQE), Blind Image Integrity Notator using DCT statistics (BLIINDS-II), and Blind Image Quality Index (BIQI), were trained on the SNR scores. Their correlation with the SNR was investigated by Zhang et al [ 18 ]. The BIQA methods for the assessment of brain MR scans were employed by Sandilya and Nirmala [ 19 ] and Osadebey et al [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

Obuchowicz

Oszust

Bielecka

et al. 2020

Entropy

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

Obuchowicz

Oszust

Bielecka

et al. 2020

Entropy

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“…The method was compared with the following 21 state‐of‐the‐art NR techniques with publicly available sourcecode: BRISQUE, NOREQI, BPRI, IL‐NIQE, HOSA, GWHGLBP, ORACLE, RATER, SCORER, QENI, GM‐LOG, SISBLIM, metricQ, SSEQ, S‐INDEX, NFERM, SEER, DEEPIQ, MEON, WaDIQaM‐NR, and SNRTOI . The NOREQI, BPRI, ORACLE, RATER, QENI, and SCORER are the most similar to NOMRIQA since they employ local features.…”

Section: Methodsmentioning

confidence: 99%

“…To determine whether the advantages of a method over other methods are statistically significant, the Wilcoxon rank‐sum test was used . In the experiments, the null hypothesis that 100 SRCC values of the two methods are samples from continuous distributions with equal medians was tested.…”

Section: Methodsmentioning

confidence: 99%

“…The BRISQUE is based on the mean substracted contrast normalization (MSCN) of an image fitted to a generalized Gaussian distribution. A similar approach to the BRISQUE that uses MSCN multidirectional‐filtered coefficients can be found in the work of Jang et al The BRISQUE and other three NR methods were trained using scores provided by the SNR in the study of Yu et al The same approach is used by Zhang et al for the assessment of brain MR images. The reconstructed brain images were assessed using BRISQUE by Sandilya and Nirmala .…”

Section: Introductionmentioning

confidence: 99%

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No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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Purpose: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve lowfrequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development. K E Y W O R D Shigh-boost filtering, image quality assessment, local features, magnetic resonance images, no-reference, subjective tests

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Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Dong,

Yang,

Wen

et al. 2023

Medical Physics

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BackgroundCine magnetic resonance (MR) images have been used for real‐time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low‐field strength face the problem of limited image quality.PurposeTo improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images.MethodsThis study employed MR images from 18 pancreatic cancer patients who received MR‐guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning‐based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal‐to‐noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference‐less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception‐based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images.ResultsSignificant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20 ms for a typical input image size of 512 × 512.ConclusionTaking advantage of the prior high‐quality positioning and planning MR images, the deep learning‐based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real‐time MRgRT.

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Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image Quality Assessment

Cited by 39 publications

References 69 publications

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

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