OSA Published byCurrent clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For 3D images, however, these models have been little explored so far. In this work, first, two novel designs of a multi-slice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multi-slice models and a volumetric model. Four different random background statistics are considered, both Gaussian (non-correlated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multi-slice models, the second proposed CHO could most closely approach the volumetric model whereas the first new CHO seems to be least affected by the number of training samples.

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Since there is a wide range of applications requiring image color difference (CD) assessment (e.g. color quantization, color mapping), a number of CD measures for images have been proposed. However, the performance evaluation of such measures often suffers from the following major flaws: (1) test images contain

Since the end-user of video-based systems is often a human observer, prediction of human perception of quality (HPoQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures, one problem is the lack of generalizability. This is mainly due to the strong dependency between HPoQ and video content. Although this problem is well-known, few existing methods directly account for the influence of video content on HPoQ. This paper propose a new method to predict HPoQ by using simple distortion measures and introducing video content features in their computation. Our methodology is based on analyzing the level of spatio-temporal activity and combining HPoQ content related parameters with simple distortion measures. Our results show that even very simple distortion measures such as PSNR and simple spatio-temporal activity measures lead to good results. Results over four different public video quality databases show that the proposed methodology, while faster and simpler, is competitive with current state-of-the-art methods, i.e., correlations between objective and subjective assessment higher than 80% and it is only two times slower than PSNR

Objective measures insufficiently describe the impact of latency therefore standard measures of user experience should be incorporated in studies. Even low latencies may be detrimental to laparoscopic surgery. Results from non-experts cannot predict the impact of latency on experienced surgeons. Copyright © 2016 John Wiley & Sons, Ltd.

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We investigate the effects of common types of image manipulation and image degradation on the perceived image quality (IQ) of digital pathology slides. The reference images in our study were digital images of animal pathology samples (gastric fundic glands of a dog and liver of a foal) stained with haematoxylin and eosin. The following 5 types of artificial manipulations were applied to the images, each very subtle (though visually discernible) and always one at a time: blurring, gamma modification, adding noise, change in color saturation, and JPG compression. Three groups of subjects: pathology experts (PE), pathology students (PS) and imaging experts (IE), assessed 6 IQ attributes in 72 single-stimulus trials. The following perceptual IQ attribute ratings were collected: overall IQ, blur disturbance, quality of contrast, noise disturbance, and quality of color saturation. Our results indicate that IQ ratings vary quite significantly with expertise, especially, PE and IE tend to judge IQ according to different criteria. In particular, IE seem notably more sensitive to noise than PE who, on the other side, tend to be sensitive to manipulations in color and gamma parameters. It remains an important question for future research to examine the impact of IQ on the diagnostic performance of PE. That should support our present findings in suggesting directions for further development of the numerical IQ metrics for digital pathology data