Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact—the decrease of image intensity toward the edges of an image—is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.

Spectral image contrast-based flow digital nanoplasmon-metry for ultrasensitive antibody detection

Background Gold nanoparticles (AuNPs) have been widely used in local surface plasmon resonance (LSPR) immunoassays for biomolecule sensing, which is primarily based on two conventional methods: absorption spectra analysis and colorimetry. The low figure of merit (FoM) of the LSPR and high-concentration AuNP requirement restrict their limit of detection (LOD), which is approximately ng to μg mL−1 in antibody detection if there is no other signal or analyte amplification. Improvements in sensitivity have been slow in recent for a long time, and pushing the boundary of the current LOD is a great challenge of current LSPR immunoassays in biosensing. Results In this work, we developed spectral image contrast-based flow digital nanoplasmon-metry (Flow DiNM) to push the LOD boundary. Comparing the scattering image brightness of AuNPs in two neighboring wavelength bands near the LSPR peak, the peak shift signal is strongly amplified and quickly detected. Introducing digital analysis, the Flow DiNM provides an ultrahigh signal-to-noise ratio and has a lower sample volume requirement. Compared to the conventional analog LSPR immunoassay, Flow DiNM for anti-BSA detection in pure samples has an LOD as low as 1 pg mL−1 within only a 15-min detection time and 500 μL sample volume. Antibody assays against spike proteins of SARS-CoV-2 in artificial saliva that contained various proteins were also conducted to validate the detection of Flow DiNM in complicated samples. Flow DiNM shows significant discrimination in detection with an LOD of 10 pg mL−1 and a broad dynamic detection range of five orders of magnitude. Conclusion Together with the quick readout time and simple operation, this work clearly demonstrated the high sensitivity and selectivity of the developed Flow DiNM in rapid antibody detection. Spectral image contrast and digital analysis further provide a new generation of LSPR immunoassay with AuNPs. Graphical Abstract

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method’s performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

Brightness Compensation for GF-3 SAR Images Based on Cycle-Consistent Adversarial Networks

GF-3 is the first C-Band full-polarimetric synthetic aperture radar (SAR) satellite with a space resolution up to 1m in China. The uneven brightness of SAR images is a problem when using GF-3 images, which makes it difficult to use and produce SAR images. In this paper, a brightness compensation method is proposed for GF-3 SAR images with unbalanced brightness in some areas based on a deep learning model named Cycle-Consistent Adversarial Networks (CycleGAN). The proposed method makes the image brightness relatively consistent, and it is compared with the MASK dodging algorithm, Wallis dodging algorithm and histogram equalization in terms of the profiles, brightness mean, standard deviation, and average gradient. Results of brightness compensation show that, the proposed method makes the inner brightness differences smaller, and the image quality is obviously improved, which provides even brightness image for subsequent applications, and has great practical significance.

Revisiting dynamic range and image enhancement ability of contemporary digital radiographic systems

Objectives: To assess the dynamic range and enhancement ability of radiographs acquired with contemporary digital systems. Methods: Five repeated periapical radiographs of human mandibles with an aluminium step-wedge were acquired using two sensor-based and three phosphor plate-based (PSP plate-based) systems and an X-ray unit at ten exposure times 0.020, 0.032, 0.063, 0.080, 0.100, 0.200, 0.320, 0.400, 0.500, and 0.630 s. All images had their brightness and contrast enhanced by two experienced oral and maxillofacial radiologists in consensus and were exported as both the original and enhanced file formats. Mean grey values were obtained from the aluminium steps and tabulated with their corresponding thicknesses for each exposure time, digital radiographic system, and file format. Images with saturated steps were excluded and the mean grey values from the remaining images were averaged to assess image brightness and the angular coefficient of the linear trendlines was generated from the relationship between mean grey values and their corresponding aluminium thicknesses to assess image contrast. Brightness and contrast values were compared using two-way ANOVA with post-hoc Tukey (α = 0.05). Results: PSP plate-based digital radiographic systems had a broader dynamic range. Longer exposure times produced original images with lower brightness and variable contrast (p < 0.05). Subjective enhancement significantly increased or reduced brightness and/or contrast in some systems (p < 0.05). Conclusions: Contemporary digital radiographic systems present different dynamic ranges and exposure-related brightness and contrast. Image enhancement may be a valuable tool at slightly suboptimal exposure times.

The effect of photographic light brightness on cup to disc ratio grading

Background Digital optic disc photographs are integral to remote telehealth ophthalmology, yet no quality control standards exist for the brightness setting of the images. This study evaluated the relationship between brightness setting and cup/disc ratio (c/d) grading among glaucoma specialists. Methods Optic disc photographs obtained during routine examinations under anesthesia were collected to construct an image library. For each optic disc, photographs were obtained at 3 light intensity settings: dark, medium, and bright. From the image library, photograph triads (dark, medium and bright) of 50 eyes (50 patients) were used to construct the study set. Nine glaucoma specialists evaluated the c/d of the study set photographs in randomized order. The relationships between the brightness levels and the c/d grading as well as graders’ years in practice and variability were evaluated. Results The c/d were graded as significantly larger in bright photographs when compared to photographs taken at the medium light intensity (0.53 vs 0.48, P < 0.001) as well as those taken at the dark setting (0.47, P < 0.001). In addition, no relationship was found between ophthalmologists’ years in practice and the variability of their c/d grading (P = 0.76). Conclusion Image brightness affects c/d grading of nonstereoscopic disc photographs. The brighter intensity is associated with larger c/d grading. Photograph brightness may be an important factor to consider when evaluating digital disc photographs.

Raspberry Pi-based mobile system of the pupil size evaluation

The article considers development of a human pupil measuring system on the Raspberry platform. The system is aimed at evaluating the variations of the human pupil diameter or area in the process of watching test images or video recordings. To reduce the interference from the eye surface the camera uses a band-stop color glass filter PS 13. In order to increase the pupil image contrast, IR LED backlighting of the eye is implemented. To provide the mobility of the system, battery power of the single-board computer was used while the registered image and the measurement results were transmitted via a Wi-Fi channel. The video camera and the single-board computer Raspberry Pi 4 with the battery bay are attached to the head-mounted flexible belt. The article gives the operation duration evaluation of the battery-supplied system. During the first stage of image processing, binarization by the threshold was performed. The threshold is determined by the image brightness histogram. Since the study is focused on relative variations of the pupil size, the pupil diameter or area values in pixels were chosen as informative parameters. The image recording and processing frequency in the system equalled 25 Hz that provides accurate recording of the pupil variations.