Evaluation of No-reference quality metrics for Ultrasound liver images

Outtas, Meriem; Zhang, Lu; Déforges, Olivier; Hamidouche, Wassim; Serir, Amina

doi:10.1109/qomex.2018.8463299

Cited by 5 publications

(9 citation statements)

References 9 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%

“…More recently, in 2018, Outtas et al worked on despeckeling of liver ultrasound images [47]. The studied methods, as well as the proposed one, were subjectively and objectively tested on a parenchymal organ.…”

Section: Ultrasoundmentioning

confidence: 99%

“…For the objective evaluation, the authors chose four NR-IQA metrics, i.e., the speckle's signalto-noise ratio (SSNR) [49], blind image quality evaluator based on scales (BIQES) [50], NIQE [41], and NIQE-K [51]. Five filters were evaluated: the anisotropic diffusion filter with memory based on speckle statistics (ADMSS) [52], the optimised Bayesian non-local means filter with block selection (OBNLM) [53], and three outputs (i.e., texture, edge, and global enhancement) of the multi-output filter based on multiplicative multiresolution decomposition (MOF-MMD) [54]. The explicit and hidden references were used as an anchor with the sixty stimuli for the subjective assessment.…”

Section: Ultrasoundmentioning

confidence: 99%

“…The absence of recommendations on subjective medical image and video quality assessment shows the existence of a very complex problem: no unique optimal solution exists for all scenarios, since medical image quality is very much influenced by the contextual and the human influential factors (IFs) [108]- [109]. Context IFs include applications (e.g., diagnosis, surgery, training), clinical factors (e.g., emergency care, lesion subtleness, clinical region of interest), requirements (e.g., real-time/offline, location), medical data (clinical information (anatomical, functional physiological), acquisition modalities (e.g., ultrasound, X-ray, MRI, EEG, ECG), data types (e.g., signal, images (monochrome, colour), video (monochrome, colour)); while human IFs include expertise (e.g., years of experience, major, cultural background, educational background, pedagogical implications [47], [55]), as well as emotional state (e.g., tiredness, stress, eyestrain [110]). When context and end-users differ, the purpose of the study may require a specific test protocol.…”

Section: Quality's Influential Factorsmentioning

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: Ultrasoundmentioning

confidence: 99%

Section: Ultrasoundmentioning

confidence: 99%

Section: Quality's Influential Factorsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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“…Parameter works with calculation statistics in image scene with local normalized coefficients of brightness for quantify losses of naturalness of image, which comes from presence of distortion. So, this way measure of quality could be evaluated holistically [14,15].…”

Section: Preprocessing Of Imagementioning

confidence: 99%

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Vilímek¹,

Kubíček²,

Kloudova³

et al. 2019

Acta Mechanica Slov.

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Kidney stones (nephrolithiasis) are among the most common kidney diseases. They are solid stones that arise from minerals dissolved in urine. The treatment of kidney stones depends primarily on the position, size and composition of the stones, as well as on the general health condition of the patient. However, early diagnosis is quite complicated. In this paper, we propose a fully automatic hybrid method for identification and features extraction of the kidney stones. Specifically, model is based on the multiregional segmentation and approximation of the kidney stone area. Methods consequently use concept of the active contours which are focused on extraction of the geometrical features. The method remarkably allowing for an objective monitoring and classification of the kidney stones. These results may play a role to overcome conventionally used clinical procedures where clinicians mark kidney stones manually, without software feedback.

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Goal oriented image quality assessment

Kiruthika

Masilamani

2021

IET Image Processing

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The area of image quality assessment(IQA) is an active research area in image processing and computer vision. All IQA algorithms reported in literature are attempting to quantify only the visual quality of the images/videos. An interesting question to be answered is that given a goal (task) and an image (with good visual quality), is the image good to achieve the goal by the best possible algorithm. In an attempt to spur the research community to answer this question, a new paradigm of IQA, called goal oriented IQA (GO-IQA), is introduced. GO-IQA is defined as given an image and a goal, predicting how good the image is to achieve that goal by the best possible algorithm. The need for GO-IQA is that if GO-IQA score is less, then any arbitrary algorithm attempting to achieve the goal will eventually fail. In this paper, considering segmentation as goal, a GO-IQA algorithm is proposed to predict how good the image is to do accurate segmentation by the best possible segmentation algorithm. A support vector regression model has been trained with features related to image segmentation along with the accuracies of a class of well-known image segmentation algorithms. In the process, we have obtained encouraging results in terms of the Pearson linear correlation coefficient and mean squared error. The predictions given by the proposed model have been validated with the scores of state of the art segmentation algorithm.

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Evaluation of No-reference quality metrics for Ultrasound liver images

Cited by 5 publications

References 9 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

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Goal oriented image quality assessment

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