Magnetic Resonance Image Quality Assessment by Using NonMaximum Suppression and Entropy Analysis An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude lowquality 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 nonmaximum 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 stateoftheart 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.

FRIQA  Permutation Entropy Deviation Index In this work, a new objective image quality assessment (IQA) framework, based on the working principle of permutation entropy (PE) is proposed. The framework is titled as Permutation Entropy Deviation Index (PEDI). The idea is to design an IQA framework that should be highly accurate as well as computationally efficient; making it viable to be used with different image processing applications, for visual quality assessments. The proposed model make use of the PE that helps in detecting and visualizing changes related to structures with correlation between successive samples instead of considering magnitudes of the signal. Thus, the proposed approach uses this property to efficiently predict image quality. The PE exploits the global variations in the local quality map for image quality assessment. With standard deviation as the pooling strategy, it is noted that permutation entropy between reference and distorted images can predict image quality with high measures of accuracy. Experimental results on subjective database, CSIQ, have shown that the proposed model outperforms most of existing SOTA image quality assessment models and highly correlates with subjective judgements.