A study on the usability of opinion-unaware no-reference natural image quality metrics in the context of medical images

Outtas, Meriem; Zhang, L.; Déforges, Olivier; Hammidouche, W.; Serir, Amina; Cavaro‐Ménard, Christine

doi:10.1109/isivc.2016.7894006

Cited by 11 publications

(8 citation statements)

References 21 publications

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“…NIQE [34] uses a circularly-symmetric Gaussian weighting function and a multivariate Gaussian model. 2stepQA-NR [6] and NIQE-K [35] combine NIQE with other algorithm components. BRISQUE [10] uses mean subtracted contrast normalized (MSCN) coefficients.…”

Section: A Nr Metric Development Strategiesmentioning

confidence: 99%

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Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Pinson

2023

IEEE Trans. on Broadcast.

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This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA). We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results). Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63. None of the NR metrics we analyzed are accurate enough to be deployed by industry. Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate. We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.

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Section: A Nr Metric Development Strategiesmentioning

confidence: 99%

“…ADMD [7] limits the scope to dermoscopy images (skin lesions). NIQE-K [35] models the opinion of radiologists when viewing ultrasound images. The ITS4S3 dataset [65] depicts subject matter used by first responders: crime scenes, fireground, prison riots, search and rescue, and cityscapes.…”

Section: B Motivation For Scope Limitationsmentioning

confidence: 99%

Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Pinson

2023

IEEE Trans. on Broadcast.

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“…However, the use of existing IQA metrics has serious limitations in the medical context [42]. The objectives of CE in the medical context are quite different [43], [44]. While in the case of natural images the objective is to measure the effect of various distortions on the perceptual quality of the image; in the medical context even if some degradation may disturb the radiologists the focus is rather on the diagnosis.…”

Section: ) Qualitative Analysismentioning

confidence: 99%

Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation

Naseem¹,

Khan²,

Satpute

et al. 2021

IEEE Access

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Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. In this paper, we propose a goal-oriented contrast enhancement method to improve tumor segmentation performance. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image in a two-step process. The first step consists of applying a two-dimensional histogram specification exploiting contextual information in the corresponding guidance image, i.e. Magnetic Resonance Image (MRI). In the second step, an optimization scheme using a structural similarity measure to preserve the structural information of the original image is performed. To the best of our knowledge, this kind of contrast enhancement optimization scheme using cross-modal guidance is proposed for the first time in the medical imaging context. The experimental results obtained on real data demonstrate the effectiveness of the method in terms of enhancement and segmentation quality in comparison to some state-of-the-art methods based on the histogram.

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“…SAMVIQ interface allowed to visualise each image several times, and to re-evaluate a previously scored image, using a continuous rating scale ranging from 0 to 100, with an explicitly labelled reference. 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].…”

Section: Ultrasoundmentioning

confidence: 99%

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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A study on the usability of opinion-unaware no-reference natural image quality metrics in the context of medical images

Cited by 11 publications

References 21 publications

Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation

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

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