Tify: A quality-based frame selection tool for improving the output of unstable biomedical imaging

Abstract

The ability to image biological tissues is critical to our understanding of a range of systems and processes. In the case of in situ living tissue, such imaging is hampered by the innate mechanical properties of the tissue. In many cases, this provides challenges in how to process large amounts of image data which may contain aberrations from movement. Generally, current tools require the provision of reference images and are unable to maintain temporal correlations within an image set. Here, we describe a tool–Tify–which can accurately predict a numerical quality score versus human scoring and can analyse image sets in a manner that allows the maintenance of temporal relationships. The tool uses regression-based techniques to link image statistics to image quality based on user provided scores from a sample of images. Scores calculated by the software correlate strongly with the scores provided by human users. We identified that, in most cases, the software requires users to score between 20–30 frames in order to be able to accurately calculate the remaining images. Importantly, our results suggest that the software can use coefficients generated from consolidated image sets to process images without the need for additional manual scoring. Finally, the tool is able to use a frame windowing technique to identify the highest quality frame from a moving window, thus retaining macro-chronological connections between frames. In summary, Tify is able to successfully predict the quality of images in an image set based on a small number of sample scores provided by end-users. This software has the potential to improve the effectiveness of biological imaging techniques where motion artefacts, even in the presence of stabilisation, pose a significant problem.

Targeting IL-36 improves age-related coronary microcirculatory dysfunction and attenuates myocardial ischemia/reperfusion injury in mice

“…Free-flowing neutrophils, which passed through the coronary microcirculation without making adhesive interactions, were counted manually over the 2-minute recorded capture. To analyze adherent neutrophil and platelet presence, captured videos were subjected to postacquisition image repair using in-house–designed software ( Tify ) in which out-of-focus frames were removed ( 54 ). Neutrophils and platelet aggregates/microthrombi were then quantitated by placing a mask around PE-Ly6G + and APC-CD41 + areas, respectively.…”

Live Intravital Imaging of Cellular Trafficking in the Cardiac Microvasculature—Beating the Odds

“…Using a cut-off value for this difference, the toolbox is able to remove frames which are considered to be too dissimilar to the reference frames. Along similar lines, we have developed a tool—termed Tify —which is able to process large image stacks and perform automated frame removal based on whether or not they meet given criteria (in terms of this review, this would be whether they contain motion artifacts or not) (96). However, rather than use reference frames, Tify instead uses a small subset of human scoring, underlying image statistics (such as standard deviation, entropy, and skewness), and regression analysis to attempt to “quality score” all frames from an entire image stack (for an idea of the methodology involved, see Figure 3).…”

“…To make this software more applicable for intravital microscopy, we built in two additional statistics which were specifically relevant to cardiac intravital imaging: sum pixel ramp and segment intensity deviation. Detailed descriptions on how these statistics are calculated is beyond the scope of this review, but detailed information is available in the published manuscript detailing Tify (96). On average, users needed to score around 20 frames in order for Tify to score the remaining frames in the image stack with high levels of accuracy.…”

“…To test this accuracy, we asked users to attribute a quality score (0–5) to 200 frames of a video obtained from cardiac intravital imaging. We then provided Tify with the scores from the first 20 of these, and assessed the software's accuracy at calculating the rest; the correlation co-efficient between human scores and calculated scores was high (e.g., 0.76 ± 0.03 for one set) (96). This approach has some advantages—because the image scores for unseen frames are calculated formulaically, Tify does not need to have seen images in advance in order to score them—it simply needs to have a formula with which to calculate the scores from.…”

Satellite Image Enhancement using Wavelet-domain based on Singular Value Decomposition

“…The performance of the visual systems can be influenced by many factors such as viewpoint and perspective, illumination, scale, deformation, and high intraclass variations. While discussing the visual information and their importance in today's life, one cannot ignore the growing demand for the quality images in many applications such as geosciences studies [2], biomedical imagining [3], astronomy [4], geographical information systems [5], surveillance [6], and remote sensing [7]. The usage of quality images is an essential requirement to achieve robust results.…”