A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images.

Augmentation of Human Action Datasets with Suboptimal Warping and Representative Data Samples.

The popularity of action recognition (AR) approaches and the need for improvement of their effectiveness require the generation of artificial samples addressing the nonlinearity of the time-space, scarcity of data points, or their variability. Therefore, in this paper, a novel approach to time series augmentation is proposed. The method improves the suboptimal warped time series generator algorithm (SPAWNER), introducing constraints based on identified AR-related problems with generated data points.

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

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.

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

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.

Interobserver variability in quality assessment of magnetic resonance images.

Background: The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment.

Methods: For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists).

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

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.

Data Augmentation with Suboptimal Warping for Time-Series Classification.

In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility.

Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures.

Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches.