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Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the "p" value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.

River-level estimation is a critical task required for the understanding of flood events and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river-camera images to estimate river levels but, currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We have developed an approach using an automated water semantic segmentation method to ease the process of river-level estimation from river-camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the rivers Severn and Avon, UK (21 November–5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the rivers Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's correlation coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river-camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.

This paper presents a novel approach for estimating wheat yields based on estimated abundances of endmembers attributed to photosynthetic pigments, using Landsat 8 images acquired during maximum gr...

OBJECTIVE: To compare the quality of various polychromatic and monochromatic images with or without using an iterative metal artifact reduction algorithm (iMAR) obtained from a dual-energy computed tomography (CT) to evaluate total knee arthroplasty. MATERIALS AND METHODS: We included 58 patients (28 male and 30 female; mean age [range], 71.4 [61-83] years) who underwent 74 knee examinations after total knee arthroplasty using dual-energy CT. CT image sets consisted of polychromatic image sets that linearly blended 80 kVp and tin-filtered 140 kVp using weighting factors of 0.4, 0, and -0.3, and monochromatic images at 130, 150, 170, and 190 keV. These image sets were obtained with and without applying iMAR, creating a total of 14 image sets. Two readers qualitatively ranked the image quality (1 [lowest quality] through 14 [highest quality]). Volumes of high- and low-density artifacts and contrast-to-noise ratios (CNRs) between the bone and fat tissue were quantitatively measured in a subset of 25 knees unaffected by metal artifacts. RESULTS: iMAR-applied, polychromatic images using weighting factors of -0.3 and 0.0 (P-0.3i and P0.0i, respectively) showed the highest image-quality rank scores (median of 14 for both by one reader and 13 and 14, respectively, by the other reader; p < 0.001). All iMAR-applied image series showed higher rank scores than the iMAR-unapplied ones. The smallest volumes of low-density artifacts were found in P-0.3i, P0.0i, and iMAR-applied monochromatic images at 130 keV. The smallest volumes of high-density artifacts were noted in P-0.3i. The CNRs were best in polychromatic images using a weighting factor of 0.4 with or without iMAR application, followed by polychromatic images using a weighting factor of 0.0 with or without iMAR application. CONCLUSION: Polychromatic images combined with iMAR application, P-0.3i and P0.0i, provided better image qualities and substantial metal artifact reduction compared with other image sets.

BACKGROUND: This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. METHODS: A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20–80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose(4) levels 1–7 and iterative model reconstruction (IMR) levels 1–3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose(4) level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. RESULTS: For the phantom study, the iDose(4) and IMR groups had significantly higher SNR and CNR than the FBP group (all P0.05), while AS from the IMR (level 1–3) image series were lower than the FBP group (all P

International audience; This paper presents and compares two combined experimental-numerical techniques for the investigation of fracture properties of additively manufactured polymer parts using digital image correlation (DIC) measurements. The first method only uses measured kinematic fields, and the second is based on Finite Element simulations driven by measured boundary conditions. A mini Single Edge Notched Tensile sample manufactured by fused filament fabrication with ABS is studied. It is shown that both methods locally extract J-integrals, and the crack tip is accurately located by the FE-based method. By comparing computed displacements to those measured via DIC, it is possible to locally check the validity of the numerical model. The initiation and propagation stages are analyzed independently thanks to two different magnifications of acquired image series.

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 μs/px dwell time. Within the NRR algorithm, the “smoothness factor” that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general, the precision of NRR images is robust over a wide range of parameters.

Rationale and Objectives Clinically-relevant quantitative measures of task-based image quality play key roles in effective optimization of medical imaging systems. Conventional phantom-based measures do not adequately reflect the real-world image quality of clinical Computed Tomography (CT) series which is most relevant for diagnostic decision-making. The assessment of detectability index which incorporates measurements of essential image quality metrics on patient CT images can overcome this limitation. Our current investigation extends and validates the technique on standard-of-care clinical cases. Materials and Methods We obtained a clinical CT image dataset from an Institutional Review Board-approved prospective study on colorectal adenocarcinoma patients for detecting hepatic metastasis. For this study, both perceptual image quality and lesion detection performance of same-patient CT image series with standard and low dose acquisitions in the same breath hold and four processing algorithms applied to each acquisition were assessed and ranked by expert radiologists. The clinical CT image dataset was processed using the previously validated method to estimate a detectability index for each known lesion size in the size distribution of hepatic lesions relevant for the imaging task and for each slice of a CT series. We then combined these lesion-size-specific and slice-specific detectability indexes with the size distribution of hepatic lesions relevant for the imaging task to compute an effective detectability index for a clinical CT imaging condition of a patient. The assessed effective detectability indexes were used to rank task-based image quality of different imaging conditions on the same patient for all patients. We compared the assessments to those by expert radiologists in the prospective study in terms of rank order agreement between the rankings of algorithmic and visual assessment of lesion detection and perceptual quality. Results Our investigation indicated that algorithmic assessment of lesion detection and perceptual quality can predict observer assessment for detecting hepatic metastasis. The algorithmic and visual assessment of lesion detection and perceptual quality are strongly correlated using both the Kendall's Tau and Spearman's Rho methods (perfect agreement has value 1): for assessment of lesion detection, 95% of the patients have rank correlation coefficients values exceeding 0.87 and 0.94, respectively, and for assessment of perceptual quality, 0.85 and 0.94, respectively. Conclusion This study used algorithmic detectability index to assess task-based image equality for detecting hepatic lesions and validated it against observer rankings on standard-of-care clinical CT cases. Our study indicates that detectability index provides a robust reflection of overall image quality for detecting hepatic lesions under clinical CT imaging conditions. This demonstrates the concept of utilizing the measure to quantitatively assess the quality of the information content that different imaging conditions can provide for the same clinical imaging task, which enables targeted optimization of clinical CT systems to minimize clinical and patient risks.

Purpose To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. Theory and methods The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging. Results The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features. Conclusions We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.

To observe the complicated physical world, sensor networks are widely used for data collection. Moreover, due to the limited energy, storage and computation capacity of sensor nodes, approximate data acquisition in adaptive sampling manner is a wide choice. Nevertheless, existing data acquisition methods are most designed for univariate data (e.g., temperature), and thus not applicable to image data with high dimensions and complex structures. In this paper, we propose a framework of Physical-world-Aware Adaptive Data Acquisition (PAADA) for image sensor networks, to sample data adaptively with pre-specified error bound. First, based on the convolutional autoencoders (CAEs), PAADA compresses the high-dimensional image data into a feature vector with a handful of hidden variables which compactly capture the key features of the image data. Second, PAADA designs a Physical-world-aware Adaptive Sampling (PAS) algorithm based on the Hermitee interpolation. Under the feature space, the PAS algorithm adjusts the sampling frequency automatically by considering the change trend of the feature vector. In addition, the feature vectors at non-sampling time points can be recovered with O (∈) approximation guarantee to the ground truths. Next, PAADA recovers the image data at non-sampling time points based on the recovered feature vectors. Finally, for each sensor, PAADA returns an image series composed of sampled images (at sampling time points) and approximate images (at non-sampling time points). Experiments on real-world datasets demonstrate that the proposed PAADA has high performance in both accuracy and energy consumption.

Joint coregistration (JC) approach, based on least square (LS) method, is proposed to improve the synthetic aperture radar (SAR) images' coregistration precision and persistent scatterer interferometric synthetic aperture radar (PS-InSAR) performance. Firstly, the image pairs with small spatial and temporal baselines are used to estimate image shifts to reduce the decorrelation effects. The SAR images are all positioned in spatial-temporal baseline plane and connected with the Delaunay triangulation method. Those arcs with long spatial and/or temporal baselines are deleted with an experienced threshold. Secondly, the shifts are estimated with arc-linked images by conventional coregistration approaches. However, the shifts of arc-unlinked images are still unknown even the arc-linked images are accurately coregistered. For this problem, we estimate the shifts of all the slave images with respect to the reference image from the shifts estimated by the short spatial and temporal baselines. LS method is used to tackle the problem of shifts' error propagation. Finally, all the slave images are resampled according to the estimated LS-method-based images' shifts. 44 ERS-2 images are used to test the performance of JC approach for PS-InSAR processing. The validity is illustrated by the amplitude and phase stability of PS candidates, as well as the deformation performance of PS-InSAR.

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFI’s performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disk confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.

Mean-Shift Super Resolution (MSSR) is a principle based on the Mean Shift theory that improves the spatial resolution in fluorescence images beyond the diffraction limit. MSSR works on low- and high-density fluorophore images, is not limited by the architecture of the detector (EM-CCD, sCMOS, or photomultiplier-based laser scanning systems) and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image series, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other analytical super resolution image approaches. Altogether, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

The use of computer-aided secondary tools that assist physicians for automatic segmentation of the lung is very important for the diagnosis of many lung diseases. Performing the segmentation of the lungs with manual selection by experts can cause human error, subjective and unnecessary waste of time. In this study, an approach based on Mask R-CNN is proposed for automatic segmentation of the lung from CT scans. The CT image series used in the study are obtained from publicly available datasets such as HUG-ILD and VESSEL12 databases. In experimental studies for lung segmentation, for the HUG-ILD dataset, 95.95% Dice similarity coefficient and 7.65% volumetric overlap error are obtained, respectively, whereas for the VESSEL12 dataset, these metrics are measured as 96.80% and 6.12%, respectively. As a result, the Mask R-CNN-based approach proposed in this study for lung segmentation is confirmed to be successful.

IntroductionDeep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e. degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT).MethodsAn IRB-approved study (NU00212113) was used to identify patients treated with lung SBRT. Pre-therapy free breathing (FB) CT images from 849 patients were input into a multi-task deep neural network to generate an image fingerprint (or DL score) that predicts time-to-event local failure outcomes. The impact of tumor motion was examined on the DL scores for a hold-out set comprising 228 image series from 19 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images.FindingsVariance in image-derived DL scores were associated with tumor motion (R2 = 0.45). Specifically, DL score variance was deterministic with undulations in phase with the respiratory cycle (R2 = 0.77-0.99). DL scores, but not tumor volumes, peaked near end-exhalation (∼50% phase). The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r = 0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p < 0.0001).InterpretationWe show that an image-based DL risk score derived from a series of four-dimensional CT images varies in a deterministic, sinusoidal trajectory in phase with the respiratory cycle. These results indicate that deep learning models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.

Image registration is a fundamental tool of remote sensing. The recent proliferatio of earth observation satellites has opened the way to the analysis of long image time series with denser temporal repetition. Given this wealth of images, it is crucial to design automatic tools to process them. We thus propose a method for the global registration of satellite image time series, that leverages their redundancy to improve in precision and robustness. By computing the relative displacement for all possible pairs of images, we are able to discard outliers and minimize the number of misaligned images. Experiments on synthetic data show that longer image series are registered with a higher precision.

Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). Traditional methods are less intelligent and have lower accuracy of segmentation. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. The study uses ILD Database-MedGIFT from 128 patients with 108 annotated image series and selects 1946 regions of interest (ROI) of lung tissue patterns for training and testing. First, images are denoised by Wiener filter. Then, segmentation is performed by fusion of features that are extracted from the gray-level co-occurrence matrix (GLCM) which is a classic texture analysis method and U-Net which is a standard convolutional neural network (CNN). The final experiment result for segmentation in terms of dice similarity coefficient (DSC) is 89.42%, which is comparable to the state-of-the-art methods. The training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation.

This research is focused on the application of water indexes derived from historical Landsat image series to quantify the impact caused by anthropic activities on wetlands. This work is focused spe...

Detecting slope movement before landslides occur in mountain regions is crucial for disaster reduction. In October 2018, a gigantic landslide occurred on the Jinsha River, causing dammed-lake breach flood 500 km downstream. In this work, we used 25 Sentinel-2 images from November 2015 to August 2018 to explore the capability of this high temporal resolution optical images in detecting slope movement before the Jinsha River landslide. Normalized difference vegetation index (NDVI) was calculated to composite temporal profiles using all Sentinel-2 images. With this NDVI time series, unsupervised K-means classifier was applied to initially classify the study area and find the best thresholds for automatically extracting landslide scars in the image series. These extracted landslide scars were validated using interpreted results from two high spatial resolution images of similar dates in 2015 (user’s accuracy 89.7%, producer’s accuracy 83.6%) and 2018 (user’s accuracy 90.8%, producer’s accuracy 74.9%). After validation, extracted landslide scars of different years were counted and displayed in an RGB composite image to highlight slope movement. In addition, monotonous decrease/increase of NDVI was also observed, indicating continuous expansion of landslide scarps and movement of landslide head along the slope on the landslide surface. This work demonstrated the capability of Sentinel-2 time series images to capture slope movement with short revisit time at low cost. By incorporating other environmental information (such as elevation), this proposed method has the potential to consistently map pre-landslide slope movements over a large region.

Various biosensing platforms for real-time monitoring and mapping of chemical signals in neural networks have been developed based on CMOS process technology. Despite their achievements, however, there remains a demand for an advanced method that can offer detailed insights into cellular functions with higher spatiotemporal resolution. Here, we present a pH image sensor that employs a high-density array of 256 × 256 pixels and readout circuitry designed for fast operation. The sensor's characteristics, such as the pH sensitivity of 55.1 mV/pH and higher frame speed of 1933 fps, are experimentally demonstrated and compared to those of state-of-the-art pH image sensors. Among them, our sensor presents the smallest pitch of 2 μm with a significantly high operation speed. This sensor can successfully detect a pH change, but also transform the measured data to a two-dimensional image series in real time. The practical spatial resolution of images is investigated by an evaluation method that we first propose in this paper. By this method, we confirm that our sensor can discriminate objects distanced over 4 μm apart, which is twice bigger than the pixel pitch. In order to analyze the degraded resolution and image blur, a capacitive coupling effect at an ion-sensitive membrane is suggested as the main factor and demonstrated by simulation.

Seam-driven approaches have been proven effective for handling imperfect image series in image stitching. Generally, seam-driven approach utilizes seam-cutting to find a best stitching seam from one or finite alignment hypotheses based on a predefined seam quality metric. However, the quality metrics in these methods are defined in order to evaluate the average performance of the pixels along the seam. In many cases, the seam with the minimal cost is not necessarily optimal in terms of human perception. In this paper, we propose a novel iterative seam estimation method where the iteration procedure is guided by our quality evaluation for the pixels along the seam. First, we estimate a stitching seam via the conventional seam-cutting and introduce a hybrid quality evaluation to evaluate the pixels along the seam; the evaluated costs are then used to recalculate the difference map of the overlapping region and re-estimate a stitching seam. This evaluation–re-estimation procedure iterates until the current seam changes negligibly comparing with the previous seams. Experiments show that comparing with the conventional seam-cutting and other seam-driven methods, our method can produce results with less artifacts and overall is more visually pleasing.

Background Chlorophyll fluorescence analysis is one of the non-invasive techniques widely used to detect and quantify the stress-induced changes in the photosynthetic apparatus. Quantitative information is obtained as a series of images and the specific fluorescence parameters are evaluated inside the regions of interest outlined separately on each leaf image. As the performance of photosynthesis is highly heterogeneous over a leaf surface, the areas of interest selected for generating numeric data are crucial for a reliable analysis. The differences in intact leaf physio-morphological characters and in the structural effects of stress between leaves increase the risk of artefacts. Results The authors propose a new enhanced method for precise assessment of stress-induced spatiotemporal changes in chlorophyll a fluorescence exemplified in the leaves of common ice plants infected with a fungal pathogen. The chl a fluorescence leaf image series obtained with Imaging-PAM fluorometer are aligned both by affine and nonlinear spline transforms based on the set of control points defined interactively. The successive readings were taken on the same leaf and this image sequence registration allows to capture quantitative changes of fluorescence parameters in time and along selected directions on the leaf surface. The time series fluorescence images of attached leaf, aligned according to the proposed method, provide a specific disease signature for an individual leaf. The results for C3 and Crassulacean Acid Metabolism (CAM) plants have been compared with respect to the type of photosynthetic metabolism and the image alignment accuracy has also been discussed. Conclusions The image alignment applied to the series of fluorescence images allows to evaluate the dynamics of biotic stress propagation in individual plant leaves with better accuracy than previous methods. An important use of this method is the ability to map the fluorescence signal horizontally in one leaf during disease development and to accurately compare the results between leaves which differ in morphology or in the structural effects of stress. This approach in analysing chlorophyll fluorescence changes can be used to receive spatial and temporal information over a sample area in leaves infected by different pathogenic fungi and bacteria. Electronic supplementary material The online version of this article (10.1186/s13007-019-0401-4) contains supplementary material, which is available to authorized users.

Purpose To compare the image quality produced by kinetic imaging in x-ray angiography and the current reference standard digital subtraction angiography (DSA). Materials and Methods This prospective observational crossover study enrolled 42 patients undergoing lower limb x-ray angiography between February and June 2017 (mean age, 68.7 years; age range, 49-89 years; 32 men [mean age, 67.1 years; age range, 49-89 years] and 10 women [mean age, 75 years; age range, 57-85 years]). Signal-to-noise ratios (SNRs) of DSA and kinetic image pairs were compared. Visual quality comparisons were also performed by specialists who used an online questionnaire. Interrater agreement was characterized by percent agreement and Fleiss k. Results A total of 1902 regions of interest were carefully selected in 110 image pairs to calculate and compare the SNRs. Median SNR in raw kinetic images was 3.3-fold and 2.3-fold higher than raw and postprocessed DSA images, respectively. A total of 232 pairs of raw and postprocessed kinetic images were compared. It was indicated that postprocessing improved the quality of kinetic images in 63.9% (2668 of 4176) of the comparisons. Interrater agreement was 75% and Fleiss k was 0.12 (P.001). Also, 238 pairs of kinetic and DSA images were compared. Kinetic imaging was judged to have provided higher quality images than DSA in 69.0% (2462 of 3570) of the comparisons. The interrater agreement was 81% and Fleiss k was 0.17 (P.001). Conclusion Kinetic imaging helps to view the same structures as digital subtraction angiography but offers better image quality. The improved signal-to-noise ratio suggests that this approach could reduce radiation exposure and improve the ability to view smaller vessels. © RSNA, 2018 Online supplemental material is available for this article.

Background and Aims As it is necessary to refrain from going out due to the COVID-19 pandemic, a system that allows dialysis patients to be treated at a remote location or at home, i.e., a home care support system, is required. Information and communications technology (ICT) used for these purposes is widely applied in various medical fields. Using ICT has the advantage of allowing the sharing of patients’ electronic patient records (EPR) among medical staff, but increases the risk of copyright infringement and privacy leaks during archiving and transmission. We have developed a home care support system for peritoneal dialysis patients using information hiding technology consisting of both digital watermarking technology for copyright protection and steganography technology for communication security when treating patients at home using ICT. In addition, we evaluated the developed system. Method The system for sharing medical information was developed in the PHP programming language on a personal computer system using Microsoft Azure cloud services. Figure 1 shows an explanation of the digital watermarking technology and steganography technology used in the developed system. 1. Digital watermarking technology The patient’s data, such as EPR data, facility name, etc., were hidden in the region of non-interest (RONI) of the patient’s chest CT image series and stored in a database. 2. Steganography technology We call scene photos “cover pictures.” Medical information (CT images, etc.) was hidden in the cover picture. In this study, the cover picture containing the medical information was designated as a Stego image. A body CT image series (16-bit, 512 × 512, 100 slices) was used to verify the steganography technique. These CT images were compressed using 7-Zip and then saved in a folder, which was then embedded in the cover photo. The Stego image was then sent from the patient’s home to the medical institution via the home care support system. Results We investigated the hash value, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the image series (Fig. 2). If the structure of the image or photograph was exactly the same, the SSIM shows a value of 1. If the PSNR is ≥ 40 dB, the image quality can be maintained without affecting diagnosis. If part of the ROI is changed during transmission, the hash value decoded from the received Stego image will be different from that before transmission. For Stego images containing watermarked or hidden CT images with 4000 words embedded, SSIM and PSNR were ≥ 0.99 dB and 65.3 dB, respectively. If the medical information was embedded in a low bit plane, such as a 1-bit or 2-bit plane, the radiologist could not identify the embedded information. When our technology was applied, there were no changes in the capacity of CT images or Stego images before and after embedding. Therefore, it was not possible to tell that medical information was embedded due to changes in capacity. Conclusion Using ICT, we have built a home care support system that can conceal medical information by combining digital watermarking technology and steganography technology to ensure the copyright of images and to ensure privacy and secure transmission of EPR and CT images. Using the developed system, daily medical information of dialysis patients could be transmitted safely to the institute, and the medical staff could share the information safely. Both techniques can be applied to all digital image information, and is not just limited to CT images.

Modern treatment in the field of head and neck surgery aims for the least invasive therapy and places great emphasis on restorative treatment, especially in the case of injury and deformation corrective surgery. More and more often, surgeons use CAD/CAM (Computer-Aided Design and Computer-Aided Manufacturing) tools in their daily practice in the form of models, templates, and computer simulations of planning. These tools are based on DICOM (Digital Imaging and Communications in Medicine) files derived from computed tomography. They can be obtained from both fan-beam (FBCT) and cone-beam tomography (CBCT) acquisitions, which are subsequently segmented in order to transform them into a 1-bit 3D model, which is the basis for further CAD processes. Aim: Evaluation of differences in the dimensions of mandible condyle models in fan- versus cone-beam computer tomography for surgical treatment purposes. Methods: 499 healthy condyles were examined in CT-based 3D models of Caucasians aged 8–88 years old. Datasets were obtained from 66 CBCT and 184 FBCT axial image series (in each case, imaging both mandible condyles resulted in the acquisition of 132 condyles from CBCT and 368 condyles from FBCT) and were transformed into three-dimensional models by digital segmentation. Eleven different measurements were performed to obtain information whether there were any differences between FBCT and CBCT models of the same anatomical region. Results: 7 of 11 dimensions were significantly higher in FBCT versus lower in CBCT (p &lt, 0.05).

In this work, we define a theoretical approach to characterizing the signal-to-noise ratio (SNR) of multi-channeled systems such as spectral computed tomography image series. Spectral image datasets encompass multiple near-simultaneous acquisitions that share information. The conventional definition of SNR is applicable to a single image and thus does not account for the interaction of information between images in a series. We propose an extension of the conventional SNR definition into a multivariate space where each image in the series is treated as a separate information channel thus defining a spectral SNR matrix. We apply this to the specific case of contrast-to-noise ratio (CNR). This matrix is able to account for the conventional CNR of each image in the series as well as a covariance weighted CNR (Cov-CNR), which accounts for the covariance between two images in the series. We evaluate this experimentally with data from an investigational photon-counting CT scanner (Siemens).

Background Coronary CT angiography (CCTA) is a complicated CT exam in comparison to other CT protocols. Exam success highly depends on image assessment of experienced radiologist and the procedure is often time-consuming. This study aims to evaluate feasibility of automatic CCTA reconstruction in 0.25 s rotation time, 16 cm coverage CT scanner with best phase selection and AI-assisted motion correction. Methods CCTA exams of 90 patients with heart rates higher than 75 bpm were included in this study. Two image series were reconstructed—one at automatically selected phase and another with additional motion correction. All reconstructions were performed without manual interaction of radiologist. A four-point Likert scale rating system was used to evaluate the image quality of coronary artery segment by two experienced radiologists, according to the 18-segment model. Analysis was done on per-segment basis. Results Total 1194 out of the 1620 segments were identified for quality evaluation in 90 patients. After automatic best phase selection, 1172 segments (98.3%) were rated as having diagnostic image quality (scores 2–4) and the average score is 3.64 ± 0.55. When motion corrections were applied, diagnostic segment number increases to 1192 (99.8%) and the average score is 3.85 ± 0.37. Conclusions With the help of 0.25 s rotation speed, 16-cm z-coverage and AI-assisted motion correction algorithm, CCTA exam reconstruction could be performed with minimum radiologist involvement and still meet image quality requirement.