The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images

Liu, Hantao; Koonen, Jos; Fuderer, Miha; Heynderickx, Ingrid

doi:10.1109/tip.2016.2561406

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…Furthermore, the analysis of variance (ANOVA) can be used to analyse potential differences between participants in terms of scoring. Outtas et al [47], Lévêque et al [3], [55], and Liu et al [34] explicitly conducted such analysis to examine the impact of participants on quality scoring.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…Low DCT compression rate, low JPEG compression quality, and high JPEG2000 compression rate resulted in high DMOS values, that is to say, poor image quality. Furthermore, a high correlation was found between the DMOS values and thirteen FR-IQA metrics studied, i.e., SNR [22], PSNR [22], SSIM [23], multi-scale SSIM (MS-SSIM) [29], feature similarity index measure (FSIM) [30], information fidelity criterion (IFC) [31], NQM [24], weighted SNR (WSNR) [24], VIF [25], VIF in pixel domain (VIFP) [25], universal image quality index (UQI) [32], information-weighted PSNR (IW-PSNR) [34], and information weighted SSIM (IW-SSIM) [33]. NQM presented the highest correlation (i.e., 0.94), whereas UQI showed a lowest correlation (i.e., 0.81).…”

Section: Magnetic Resonance (Mr) Imagingmentioning

confidence: 99%

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Comparative study of the methodologies used for subjective medical image quality assessment

et al. 2021

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Healthcare professionals have been increasingly viewing medical images and videos in their routine clinical practice, and this in a wide variety of environments. Both the perception and interpretation of medical visual information, across all branches of practice or medical specialties (e.g., diagnostic, therapeutic, or surgical medicine), career stages, and practice settings (e.g., emergency care), appear to be critical for patient care. However, medical images and videos are not self-explanatory and, therefore, need to be interpreted by humans, i.e., medical experts. In addition, various types of degradations and artifacts may appear during image acquisition or processing, and consequently affect medical imaging data. Such distortions tend to impact viewers' quality of experience, as well as their clinical practice. It is accordingly essential to better understand how medical experts perceive the quality of visual content. Thankfully, progress has been made in the recent literature towards such understanding. In this article, we present an up-to-date state of the art of relatively recent (i.e., not older than ten years old) existing studies on the subjective quality assessment of medical images and videos, as well as research works using task-based approaches. Furthermore, we discuss the merits and drawbacks of the methodologies used, and we provide recommendations about experimental designs and statistical processes to evaluate the perception of medical images and videos for future studies, which could then be used to optimise the visual experience of image readers in real clinical practice. Finally, we tackle the issue of the lack of available annotated medical image and video quality databases, which appear to be indispensable for the development of new dedicated objective metrics.

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Section: Data Analysis Methodsmentioning

confidence: 99%

Section: Magnetic Resonance (Mr) Imagingmentioning

confidence: 99%

Comparative study of the methodologies used for subjective medical image quality assessment

et al. 2021

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“…Some studies on subjective MRI-QA may be found in [3], [4], [6]- [8]. As for objective quality assessment, most efforts apply common reference metrics, such as the Peak Signal-to-Noise Ratio (PSNR) [6], [9]- [12] and the Structural Similarity Index (SSIM) [6], [9], [10].…”

Section: Background and Related Workmentioning

confidence: 99%

“…In the case of MRI, system-related factors include magnetic field (B 0 ) inhomogeneity, electrical system noise or variable coil penetration depths, whilst context factors include resonance frequency shifts between different tissues or inadequate sequence parametrization [2]. Several types of image quality impairments might be induced, including white noise artifacts [3], [4], blurring [2], [3], ghosting [4], inhomogeneities in signal intensities [2] or geometric distortions [2].…”

Section: Introductionmentioning

confidence: 99%

A Quality of Recognition Case Study: Texture-based Segmentation and MRI Quality Assessment

Rodrigues

Pinheiro

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Muscle texture may be used as a descriptive feature for the segmentation of skeletal muscle in Magnetic Resonance Images (MRI). However, MRI acquisition is not always ideal and the texture richness might become compromised. Moreover, the research for the development of texture quality metrics, and particularly no-reference metrics, to be applied to the specific context of MRI is still in a very early stage. In this paper, a case study is established from a texture-based segmentation approach for skeletal muscle, which was tested in a thigh Dixon MRI database. Upon the obtained performance measures, the relation between objective image quality and the texture MRI richness is explored, considering a set of state-of-the-art no-reference image quality metrics. A discussion on the effectiveness of existing quality assessment methods in measuring MRI texture quality is carried out, based on Pearson and Spearman correlation outcomes.

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“…Imaging modalities other than x-ray and MRI remain largely ignorant of the task-specific approach and instead rely on the pIQ, 10 while making the assumption that perceived quality is correlated with clinical performance. In fact, a number of studies continue to this day using pIQ, even in x-ray 11 and MR imaging 12 …”

Section: Introductionmentioning

confidence: 99%

Influence of study design on digital pathology image quality evaluation: the need to define a clinical task

et al. 2017

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Abstract. Despite the current rapid advance in technologies for whole slide imaging, there is still no scientific consensus on the recommended methodology for image quality assessment of digital pathology slides. For medical images in general, it has been recommended to assess image quality in terms of doctors' success rates in performing a specific clinical task while using the images (clinical image quality, cIQ). However, digital pathology is a new modality, and already identifying the appropriate task is difficult. In an alternative common approach, humans are asked to do a simpler task such as rating overall image quality (perceived image quality, pIQ), but that involves the risk of nonclinically relevant findings due to an unknown relationship between the pIQ and cIQ. In this study, we explored three different experimental protocols: (1) conducting a clinical task (detecting inclusion bodies), (2) rating image similarity and preference, and (3) rating the overall image quality. Additionally, within protocol 1, overall quality ratings were also collected (task-aware pIQ). The experiments were done by diagnostic veterinary pathologists in the context of evaluating the quality of hematoxylin and eosin-stained digital pathology slides of animal tissue samples under several common image alterations: additive noise, blurring, change in gamma, change in color saturation, and JPG compression. While the size of our experiments was small and prevents drawing strong conclusions, the results suggest the need to define a clinical task. Importantly, the pIQ data collected under protocols 2 and 3 did not always rank the image alterations the same as their cIQ from protocol 1, warning against using conventional pIQ to predict cIQ. At the same time, there was a correlation between the cIQ and task-aware pIQ ratings from protocol 1, suggesting that the clinical experiment context (set by specifying the clinical task) may affect human visual attention and bring focus to their criteria of image quality. Further research is needed to assess whether and for which purposes (e.g., preclinical testing) task-aware pIQ ratings could substitute cIQ for a given clinical task. © The Authors. Published by SPIE under aCreative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images

Cited by 17 publications

References 33 publications

Comparative study of the methodologies used for subjective medical image quality assessment

Comparative study of the methodologies used for subjective medical image quality assessment

A Quality of Recognition Case Study: Texture-based Segmentation and MRI Quality Assessment

Influence of study design on digital pathology image quality evaluation: the need to define a clinical task

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