A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

Piórkowski

et al. 2021

Sensors

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.

Section: Introductionmentioning

confidence: 99%

Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment

Stępień

Piórkowski

et al. 2021

Sensors

“…The first region is used for measuring the signal, while the second one measures the noise. Due to different characteristics of examined tissues, the application of SNR in MR‐IQA is considered challenging . In the literature, only a few NR methods have been proposed for MR‐IQA, based on achievements of NR measures designed for natural images and taking into account the specificity of MR scans.…”

Section: Introductionmentioning

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%

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

Oszust

Piórkowski

Magnetic Resonance in Med

2020

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

“…In the literature, several BIQA methods have been introduced. Interestingly, as the Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) are frequently used for the assessment of medical images [ 14 ], they are often criticized due to the need of indication of clearly defined regions with tissue and background [ 14 , 15 , 16 ]. Considering MR images, Chow and Rajagopal [ 13 ] adapted Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) [ 17 ] by training it on MR images instead of natural images.…”

Section: Introductionmentioning

confidence: 99%

“…In that work, the characteristics of MR scans were taken into account by employing a multidirectional-filtering of images. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Oszust

Bielecka

et al. 2020

Entropy

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.