Search

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the “surface image removal” technique and the method of “high dynamic range” photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images.

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY open access

The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists’ assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cut-off values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists’ assessments using local features. However, more than local features are needed to reach optimal discrimination.

BACKGROUND AND OBJECTIVE: To describe a method for averaging ocular coherence tomography angiography (OCTA) images using a commercially available photo editing software: Adobe Photoshop CC 2017. PATIENTS AND METHODS: This single-center study assessed the feasibility of using Adobe Photoshop as an OCTA image averaging tool. Three 3.0 mm × 3.0 mm OCTA images from each eye were obtained using commercially available OCTA devices. Captured OCTA images were exported in high-resolution TIFF format, imported as an image stack, aligned using an automated function, and averaged by creating a Smart Object using Photoshop CC 2017 software. In conjunction with qualitative assessment, the main outcome of the study was image grader preferences with respect to clarity of the foveal avascular zone (FAZ), blood vessel delineation, and the ability to identify abnormal vasculature. RESULTS: After removing OCTA scans with significant image distortion, 25 sets of images were included in the analysis. Adobe Photoshop CC 2017 successfully aligned and averaged all images of the superficial and deep retinal plexuses that contained a minimum 40% overlap. Three independent retinal specialists found the averaged images to be slightly or definitely preferable to the original 87%, 89%, and 69% of the time with respect to clarity of the FAZ, clarity of blood vessel delineation, and the ability to identify abnormal vasculature, respectively. CONCLUSIONS: Adobe Photoshop CC 2017 is an excellent tool for image averaging, producing high-quality resulting OCTA images. As an easily accessible software, Photoshop has the potential for use in a diversity of pathological conditions. [ Ophthalmic Surg Lasers Imaging Retina . 2019;50:802–807.]

Background Each ultrafast dynamic contrast-enhanced (DCE) MRI sequence for breast cancer generates thousands of images in a 4D stack that need to be reviewed by a radiologist. Purpose To assess whether color intensity projections (CIP) effectively summarizes-using only the time of arrival (ToA) and amount of signal enhancement (AoE) of the contrast agent-the thousands of ultrafast images. Study type Retrospective cohort clinical trial. Subjects The study included 89 patients who had been scanned with an MRI beast protocol, of which 26 had breast cancer and 63 did not. Field strength/sequence The 115-second ultrafast DCE sequence at 3T acquired 19 consecutive frames every 4.26 seconds with 152 slices per frame, yielding a 4D stack with 2888 2D images for each of water and fat. Assessment For each slice of the water 4D stack a single CIP image was generated that encoded the ToA in the hue (red, orange, yellow, green, cyan, blue) and AoE in the brightness. Each of three experienced radiologists assigned a Breast Imaging and Reporting Data System (BI-RADS) score for each patient, first using only the CIP images, and subsequently using both CIP and the full 4D stack. Statistical tests The one-sided Fisher's exact test was used to determine statistical significance of both the sensitivity and specificity between the CIP alone and the CIP plus 4D stack. Results All malignancies were detected using only CIP by at least one of the radiologists. The CIP and CIP+4D sensitivities for reader 1 were 96% and 96% (P = 0.57), specificities were 59% and 65% (P = 0.29). For reader 2, the values were 96% and 100% (P = 0.51) with 62% and 71% (P = 0.17). For reader 3 the values were 92% and 96% (P = 0.50) with 51% and 62% (P = 0.07). Data conclusion With a 95% sensitivity, CIP provides an effective summary of ultrafast DCE images of breast cancer. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1391-1399.

Classical histological stained sections have the disadvantage that fine structures, like individual neurites, or specific macromolecules, like neurotransmitters cannot be visualized. Due to its highly specific staining of only one target molecule within the cell, the visualization of delicate structures, which would be superimposed by other tissue layers in classical Azan staining, is possible with immunohistochemistry. However, using immunohistological methods not all tissues of a specimen can be visualized at once. In contrast, density specific stains like Azan allow for a whole staining of the tissues. We provide a step by step protocol of how to combine immunohistochemistry and Azan staining in the same serial paraffin sections. The combination of both methods allows for a highly detailed investigation of structures of interest. The spatial detection of the previous, to Azan staining, gained antibody-labeled signal allows for a much better understanding of animal organ systems. By using serial sections, it is possible to create an aligned image stack that is both Azan stained and also antibody-labeled. Thus enabling a correlative approach that bridges traditional histology with immunohistochemistry in animal morphology.

Even though teeth are often included in the field of view for a variety of medical CT studies, dental pathology is often missed by radiologists. Given the myriad morbidity and occasional mortality associated with sequelae of dental pathology, an important goal is to decrease these false negatives. However, given the ever-increasing volume of cases studies that radiologists have to read and the number of structures and diseases they have to evaluate, it is important not to place undue time restraints on the radiologist to this end. We hypothesized that generating panoramic dental radiographs from non-dental CT scans can permit identification of key diseases, while not adding much time to interpretation. The key advantage of panoramic dental radiographs is that they display the plane of the teeth in two dimensions, thereby facilitating fast and accurate assessment. We found that interpreting panoramic radiographic reconstructions compared to the full CT volumes reduced time-to-diagnosis of key dental pathology on average by roughly a factor of four. This expedition was statistically significant, and the average time-to-diagnosis for panoramic reconstructions was on the order of seconds, without a loss in accuracy compared to full CT. As such, we posit that panoramic reconstruction can serve as a one-slice additional series in any CT image stack that includes the teeth in its field of view.

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models, (ii) trimmed mean, (iii) median, (iv) intensity mean, and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0 . 11 / km 2 , when considering military vehicles concealed in a forest.

Background Deep learning models are turning out to be increasingly popular in biomedical image processing. The fruitful utilization of these models, in most cases, is substantially restricted by the complicated configuration of computational environments, resulting in the noteworthy increment of the time and endeavors to reproduce the outcomes of the models. New method We thus present a Docker-based method for better use of deep learning models and quicker reproduction of model performance for multiple data sources, permitting progressively more biomedical scientists to attempt the new technology conveniently in their domain. Here, we introduce a Docker-powered deep learning model, named as DDeep3M and validated it with the electron microscopy data volumes (microscale). Results DDeep3M is utilized to the 3D optical microscopy image stack in mouse brain for the image segmentation (mesoscale). It achieves high accuracy on both vessels and somata structures with all the recall/precision scores and Dice indexes over 0.96. DDeep3M also reports the state-of-the-art performance in the MRI data (macroscale) for brain tumor segmentation. Comparison with existing methods We compare the performance and efficiency of DDeep3M with three existing models on image datasets varying from micro- to macro-scales. Conclusion DDeep3M is a friendly, convenient and efficient tool for image segmentations in biomedical research. DDeep3M is open sourced with the codes and pretrained model weights available at https://github.com/cakuba/DDeep3m .

Generation of three-dimensional SEM micrographs is traditionally based on a pair of images showing the object from two different perspectives. Here a method is described by which the 3D effect can be obtained from a single photograph. This technique is founded upon the principle of object-depth mapping, where different depth levels of the object are encoded with different colors. This process enables the creation of an image stack that forms the basis for the 3D image. Several photographic examples from different scientific fields are provided in order to demonstrate the effectiveness of the method.

Analysis of three-dimensional biological samples is critical to understanding tissue function and the mechanisms of disease. Many chronic conditions, like neurodegenerative diseases and cancers, correlate with complex tissue changes that are difficult to explore using two-dimensional histology. While three-dimensional techniques such as confocal and light-sheet microscopy are well-established, they are time consuming, require expensive instrumentation, and are limited to small tissue volumes. Three-dimensional microscopy is therefore impractical in clinical settings and often limited to core facilities at major research institutions. There would be a tremendous benefit to providing clinicians and researchers with the ability to routinely image large three-dimensional tissue volumes at cellular resolution. In this paper, we propose an imaging methodology that enables fast and inexpensive three-dimensional imaging that can be readily integrated into current histology pipelines. This method relies on block-face imaging of paraffin-embedded samples using deep-ultraviolet excitation. The imaged surface is then ablated to reveal the next tissue section for imaging. The final image stack is then aligned and reconstructed to provide tissue models that exceed the depth and resolution achievable with modern three-dimensional imaging systems.

This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.

Whole slide imaging (WSI) is the core technology of pathology digitization, and the performance of WSI is determined by the speed and accuracy of autofocusing. However, the conventional autofocusing methods need to shoot multiple defocus tiles to create an image stack, or require a complex hardware modulation optical system, thereby restricting the applications in the real scenario of WSI. In this paper, we develop a deep one-shot autofocusing network of WSI for tile-wise virtual autofocusing to generate the in-focus image directly from one possible defocus image. Our method works with one image taken at any defocus distance, avoiding repeated focusing movements and camera shooting processes in principle. Experimental results demonstrate that our scheme enjoys the merits of high throughput, high speed, low cost, compatibility and offline processing.

Micro-computed X-ray tomography (μCT) coupled with visualization techniques such as three-dimensional reconstruction of internal morphological structures has opened up new pathways for analyzing the anatomy of nervous systems in intact specimens. The possibility for combining μCT with other techniques is one of the major advantages of μCT scanning, and the technical development of higher resolutions in lab-based μCT-scanners allows for investigating the anatomy of specimens in the sub-milimeter range. The European shore crab Carcinus maenas features a larval development over four zoeal and one megalopal stage with body lengths ranging from 500 μm to 2000 μm. The developing nervous system in the larvae of C. maenas is organized into a central brain which is connected via esophageal connectives with a ventral nerve chord and segmental ganglia. Since soft tissues such as the nervous tissues feature low contrasts compared to other tissues such as muscles or cuticularized body parts, the interpretation in μCT scans is challenging and needs some practice. The protocol described here is also applicable for larger specimens of a variety of species and spans over 2-3 days resulting in an image stack ready for postprocessing and visualization.

The quality and stability of materials or products containing natural fibres (textiles, fleece, fibre-reinforced materials) is mainly determined by the length of the employed natural fibres. To this day, the only possibility for determining the length distribution of natural fibres in a fibre bundle consisting of irregularly arranged fibres is to either manually sort and classify the single fibres or use a measuring instrument called Almeter. The device has not been produced for 20 years and is getting outdated with respect to hardware-interface and operating system compatibility. However, more than 500 of these devices are still in use all over the world. In this paper we present a novel method and device for optically determining the length distribution of fibres and fibre bundles with lengths greater than 5 cm. A fibre or fibre bundle consisting of fibres with varying lengths is put on a needle bed where a gripper pulls the fibres at their one end. During this draw-out process a laser line is projected onto the fibres and the reflected light is detected by a line sensor taking grey-scale pictures with a frequency of (currently) 8 kHz, but this can be easily increased by a factor of 3 or 4 with current line sensor technology. The image stack can be analysed, and the lengths of the fibres may be determined. To date, owing to the dimensions of the pre-existing apparatus that has been modified, the maximum length of fibres to be measured is 350 mm. The resolution of the optical device has been proven to be 20 microns. In addition to that, the distribution of the width of single fibres can be determined, as well. The measuring method may be easily extended to other measurement parameters, such as fibre colour. An additional advantageous feature of the novel measuring method is the possibility to adjust the resolving powers in two perpendicular directions to given desired values independently.

We investigate whether humans need to be shown the entire image stack (3D) or only the central slice (2D) of the lesion of breast tomosynthesis images in signal-known-exactly detection experiments. A directional small-scale breast tissue model based on random power-law noise was used. Assuming a breast tomosynthesis geometry, the tissue volumes were projected and reconstructed forming volumes-of-interest (VOI)s. Three different sizes of spheres with blurred edges were used to simulate lesions. The spheres were added on the VOIs to represent signal-present VOIs. Signal-present and signalabsent VOIs were presented during 2-alternative forced-choice experiments to 5 human observers in two modes; (i) 3D mode, in which all slices of the VOI were repeatedly displayed in cine mode; and in (ii) 2D mode, in which only the central slice of the reconstructed VOI (where the signal-present VOIs contained the center of the spherical lesion) was displayed using 2-alternative forced-choice experiments. Percent correct (PC) of the detection performance of all observers was evaluated. No significant differences were found systematically in the PC for the 3D and 2D image viewing for this type of backgrounds. We plan to investigate these further, along with the development of a model observer that correlates well with human performance in tomosynthesis.

Objectives The purpose was to create a time sequential three-dimensional virtual reality model, also referred to as a four-dimensional model, to explore its possible benefit and clinical applications. We hypothesized that this novel solution allows for the visuospatial benefits of the 3D model and the dynamic benefits of other existing imaging modalities. Background We have seen how 3D models hold great value in medical decision making by eliminating the variable visuospatial skills of practitioners. They have proved especially invaluable concerning the correction of congenital heart defects and have altered the course of many surgeries. There are, however, limitations to three-dimensional models. The static models only show what the heart looks like in one snapshot of its cycle and do not allow for an understanding of the physiological and dynamic processes. Methods This solution segments a 3D heart derived from a 2D image stack, times the 18 phases of a cardiac cycle and creates a 4D model that can be manipulated in space and time through the use of virtual reality. Results We believe the 4D heart provides a unique understanding of in situ cardiac anatomy not possible with other imaging techniques. Our expanding case series of clinician experiences and their immediate recognition of the potency of this technique is highly encouraging and reveals the future of functional and dynamic 4D representations of anatomy. Conclusions The 4D heart improved our understanding around complex 3D relationships over time. We propose time and effort dedicated to 4D cardiac imaging analysis of dynamic cardiac pathologies such as hypertrophic obstructive cardiomyopathy or a pre-op Rastelli repair with a narrow outflow tract could offer tremendous insight into the medical decision-making process. Electronic supplementary material The online version of this article (10.1186/s41205-018-0034-7) contains supplementary material, which is available to authorized users.

We have developed a method for accelerating the convergence of iterative least-squares migration. The algorithm uses a pseudodifferential scaling (dip and spatially varying filter) preconditioner together with a variant of conjugate gradient (CG) iteration with iterate-dependent (flexible) preconditioning. The migration is formulated without the image stack, thus producing a shot-dependent image volume that retains offset information useful for velocity updating and amplitude variation with offset analysis. Numerical experiments indicate that flexible preconditioning with pseudodifferential scaling not only attains considerably smaller data misfit and gradient error for a given computational effort, but also produces higher resolution image volumes with more balanced amplitude and fewer artifacts than is achieved with a nonpreconditioned CG method.

A well-preserved ostracod in Baltic amber has been analysed with noninvasive methods by synchrotron radiation. A micro-tomographic image stack was prepared. The appendages are visualised as images and drawings are based on photographs of different images planes provided. The result is a description of a new species, Cypria kempfi, from the Eocene of Northern Germany. The way the fossilisation of this aquatic animal may have occurred is discussed. The possible ecological preferences of the species are considered.

We report on an experimental investigation of adiabatic bubbly two-phase flow development in a DN50 pipe with a ring-shaped and a baffle-shaped constriction at different superficial velocities of gas (up to jg = 0.1400 m·s−1) and liquid (up to jl = 1.6110 m·s-1) using ultrafast electron beam X-ray computed tomography (UFXCT). From UFXCT images, cross-sectional gas holdup distributions were obtained with a temporal resolution of up to 2,500 frames per second in 18 scanning planes along the pipe. A sophisticated data processing approach was applied to extract gas holdup data immediately from the two-phase flow image stack. Based on that, time-averaged gas holdup of the cross-section and the axial center of the pipe were calculated. In addition, bubble sizes and velocities were determined.

Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a major impediment. In order to solve this issue, especially for sparse segmentation, we used a CNN with a minimal training dataset. We segmented a Cerebellar Purkinje cell from an image stack of a mouse Cerebellum cortex in less than two working days, which is much shorter than that of the conventional method. We concluded that we can reduce the total labor time for the sparse segmentation by reducing the training dataset.