On the Subjective Assessment of the Perceived Quality of Medical Images and Videos

Lévêque, Lucie; Liu, Hantao; Baraković, Sabina; Husić, Jasmina Baraković; Martini, Maria G.; Outtas, Meriem; Zhang, Lu; Kumcu, Asli; Platiša, Ljiljana; Rodrigues, Rafael; Pinheiro, António; Skodras, Athanassios

doi:10.1109/qomex.2018.8463297

Cited by 21 publications

(16 citation statements)

References 20 publications

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“…The work of Cserkaszky et al also highlights the potential for nuclear medicine and points out research synergies. Furthermore, the paper of Lévêque et al considers light field in their analysis on the perceived quality of medical contents.…”

Section: The Perceived Quality Of Light‐field Visualizationmentioning

confidence: 99%

Angularly continuous light‐field format: Concept, implementation, and evaluation

et al. 2019

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In this paper, we present the concept, the implementation, and the evaluation of our novel angularly continuous light-field format. We compared the subjective visual performance of our format with the perspective camera format through a series of subjective and objective tests. In our extensive subjective study, we used multiple absolute and comparative rating scales and various visual stimuli with different contents and angular resolutions. The perceived quality was assessed by a total of 36 test participants, who were separated based on their scientific expertise. The objective quality was evaluated through the degradations caused by three well-known compression methods. The obtained results indicate that our light-field format may outperform the conventional format, and it generally can provide at least equivalent visual quality. Furthermore, these findings open the way for data size optimization, without compromising the achieved level of perceived quality.

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Section: The Perceived Quality Of Light‐field Visualizationmentioning

confidence: 99%

Angularly continuous light‐field format: Concept, implementation, and evaluation

et al. 2019

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“…The recent advancement of medical imaging has increased attention to the development of image processing methods supporting the diagnosis of diseases. Since Magnetic Resonance (MR) images can be a subject of a variety of image processing operations, including acquisition, compression, reconstruction, or enhancement, their content can be distorted . Furthermore, patient noncompliance or operator mistakes may result in the contamination of acquired images .…”

Section: Introductionmentioning

confidence: 99%

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

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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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

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“…For medical images, a standard recommendation is still missing. For a review on the methods used in the medical imaging area, the reader can refer to [3]. Subjective IQA is a very costly and tedious process that makes it unsuitable to implement in real time.…”

Section: Introductionmentioning

confidence: 99%

Fundus image quality assessment: survey, challenges, and future scope

Raj

Tiwari

Martini

2019

IET Image Processing

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Various ocular diseases, such as cataract, diabetic retinopathy, and glaucoma have affected a large proportion of the population worldwide. In ophthalmology, fundus photography is used for the diagnosis of such retinal disorders. Nowadays, the setup of fundus image acquisition has changed from a fixed position to portable devices, making acquisition more vulnerable to distortions. However, a trustworthy diagnosis solely relies upon the quality of the fundus image. In recent years, fundus image quality assessment (IQA) has drawn much attention from researchers. This paper presents a detailed survey of the fundus IQA research. The survey covers a comprehensive discussion on the factors affecting the fundus image quality and the real-time distortions. The fundus IQA algorithms have been analyzed on the basis of the methodologies used and divided into three classes, namely: (i) Similarity-based, (ii) Segmentation-based, and (iii) Machine learning based. In addition, limitations of state of the art in this research field are also presented with the possible solutions. The objective of this paper is to provide a detailed information about the fundus IQA research with its significance, present status, limitations, and future scope. To the best of our knowledge, this is the first survey paper on the fundus IQA research.

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On the Subjective Assessment of the Perceived Quality of Medical Images and Videos

Cited by 21 publications

References 20 publications

Angularly continuous light‐field format: Concept, implementation, and evaluation

Angularly continuous light‐field format: Concept, implementation, and evaluation

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

Fundus image quality assessment: survey, challenges, and future scope

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