Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow, Li Sze; Rajagopal, Heshalini; Raveendran, P.

doi:10.1016/j.mri.2016.03.006

Cited by 43 publications

(35 citation statements)

References 36 publications

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“…However, this kind of approaches leads to new obstacles due to uncontrollable motion and particularly the different imaging characteristics. Comparatively, no-reference MIQA algorithms are more useful and challenging, and no reference information can be borrowed [ 20 , 23 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

Dai

Wang

et al. 2018

BMC Med Imaging

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BackgroundQuality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.MethodsIn total, 192, 88, 76 and 55 brain images are acquired using T2*, T1, T2 and contrast-enhanced T1 (T1C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.ResultsTo the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, pw ≥ 0.11; paired-sample t test, pp ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, picc ≥ 0.74). Furthermore, Pearson correlation coefficient (rp) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (rp ≥ 0.78), BRISQUE and NIQE in T1 (rp ≥ 0.77), BLIINDS-II in T2 (rp ≥ 0.68) and BRISQUE and NIQE in T1C (rp ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (rp ≥ 0.63) and in T2 (rp ≥ 0.64) weighted MR images.ConclusionsThe consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.

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Section: Introductionmentioning

confidence: 99%

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

Dai

Wang

et al. 2018

BMC Med Imaging

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“…VIF is an image quality evaluation index which was proposed based on the natural image statistical model, image distortion model, and human visual system model. The greater the value of VIF, the better the image quality is [ 37 , 38 ]. Table 3 gives the average values of various objective evaluation indexes for enhancing 17 low-light images by different algorithms.…”

Section: Experiments Resultsmentioning

confidence: 99%

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

et al. 2018

Sensors

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Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

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“…On the other hand, deeper networks will also be considered for representative features in image sharpness. In addition, with the public accessibility to the real-life blurring image databases of BID2011 [37] and CID2013 [66], it will be interesting to explore the proposed algorithm for more general and more practical applications [32, 67, 68]. …”

Section: Resultsmentioning

confidence: 99%

A shallow convolutional neural network for blind image sharpness assessment

Wang

et al. 2017

PLoS ONE

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Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment.

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Correlation between subjective and objective assessment of magnetic resonance (MR) images

Cited by 43 publications

References 36 publications

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

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

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

A shallow convolutional neural network for blind image sharpness assessment

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