Cost-efficient anthropomorphic head phantom for quantitative image quality assessment in cone beam CT

In this study, a novel anthropomorphic head phantom for quantitative image quality assessment in cone beam computed tomography (CBCT) is proposed. The phantom is composed of tissue equivalent materials (TEMs) which are suitable for cost-efficient fabrication methods such as silicone casting and 3D printing. A monocalcium phosphate/gypsum...

Image quality assessment and image fusion for electron tomography

Electron tomography is a powerful tool in materials science to characterize nanostructures in three dimensions (3D). In scanning transmission electron microscopy (STEM), the sample under study is exposed to a focused electron beam and tilted to obtain twodimensional (2D) projections at different angles; many imaging modes are available such as...

Objective Image Quality Assessment and Comparison of Ultrasound Probes: Research to Application of Trans-Balloon, Miniaturized TEE in Atrial Ablation Procedures

Atrial Fibrillation (AF) is a common type of arrhythmia, characterized by rapid and irregular contraction of the atria. Irregular contraction of the atria is caused by erroneous electrical activation, originating from the tissue around the Pulmonary Veins (PV) in the Left Atrium (LA). Catheter-based treatment of AF includes ablation of the...

Parameter Selection for Regularized Electron Tomography Without a Reference Image

Regularization has been introduced to electron tomography for enhancing the reconstruction quality. Since over-regularization smears out sharp edges and under-regularization leaves the image too noisy, finding the optimal regularization strength is crucial. To this end, one can either manually tune regularization parameters by trial and error...

No-Reference Weighting Factor Selection for Bimodal Tomography

Bimodal tomography introduces a weighting factor α to incorporate X-ray data into projection images acquired from scanning transmission electron microscope (STEM) for achieving an atom-specific three-dimensional (3D) reconstruction of an object on the nanoscale. Currently its value is chosen by computing reconstructions for a large range of...

Generating virtual CT from MRI using fully convolutional neural networks with improved structure quality

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Active Learning in Image Quality Assessment

A world without digital images is unthinkable in this era of information and communication technology. Billions of images are created, shared and ultimately enjoyed by users every day. However, digital images are sensitive to a wide variety of distortions during the delivery mechanism it goes through. Any of the distortions that can arise during...

Issues in the design of a no-reference metric for perceived blur

Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference (NR) type, which implies that quality prediction is based on the distorted image only. Recent...

Interactions of visual attention and quality perception

Several attempts to integrate visual saliency information in quality metrics are described in literature, albeit with contradictory results. The way saliency is integrated in quality metrics should reflect the mechanisms underlying the interaction between image quality assessment and visual attention. This interaction is actually two-fold: (1)...

Issues in the design of a no-reference metric for perceived blur

Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference (NR) type, which implies that quality prediction is based on the distorted image only. Recent...

The Effect of Task on Visual Attention and its Application to Image Quality Assessment Metrics

Determining the perceived quality of a digital image can be done by showing it to a large group of people and ask them to rate its quality. However, this method is too cumbersome and time-consuming for most applications. Therefore, automatic metrics have been developed, which are able to objectively predict the perceived image quality without...

No-Reference image quality assessment based on localized gradient statistics: Application to JPEG and JPEG2000