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Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%-50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

Funding: WWF (UK) supported PNT under grant GB095701, which also provided funds to develop the training datasets. Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing has been used extensively; however, volumes of such data are increasing, necessitating some level of automation, either for complete counting, or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster-RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations and demonstrably improve monitoring efforts from aerial imagery. Publisher PDF

PURPOSE Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan. METHOD Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion induced artifacts. The artifacts induced by rotation, translation, oscillation and any possible combination are considered. Taking advantage of the powerful learning ability of neural network, we designed a novel 3D network structure with both a large reception field and a high image resolution to map the artifact free images from artifact contaminated images. Quantitative results of the proposed method were evaluated against the results of U-Net and proposed networks without dilation structure. 30 sets of motion contaminated images from two hospitals were selected to do clinical evaluation. RESULT Facilitating the training dataset with artifacts induced by variable motion patterns and the neural network, the artifact can be removed with good performance. Validation dataset with simulated random motion pattern showed outperformed image correction, and quantitative results showed the proposed network had the lowest normalized root mean-square-error, highest peak signal-to-noise ratio and structure similarity, indicating our network gave the best approximation of gold standard. Clinical image processing results further confirmed the effectiveness of our method. CONCLUSION We proposed a novel deep learning based algorithm to eliminate motion artifacts. The CNNs trained with synthesized image pairs achieved promising result in artifacts reduction. The corrected images increased the diagnostic confidence compared with artifacts contaminated images. We believe that the correction method can restore the ability to successfully diagnose and avoid repeated CT scans in certain clinical circumstances. This article is protected by copyright. All rights reserved.

BACKGROUND Magnetic resonance imaging (MRI)-targeted prostate biopsy is a routinely used diagnostic tool for prostate cancer (PCa) detection. However, a clear superiority of the optimal approach for software-based MRI processing during biopsy procedures is still unanswered. To investigate the impact of robotic approach and software-based image processing (rigid vs. elastic) during MRI/transrectal ultrasound (TRUS) fusion prostate biopsy (FBx) on overall and clinically significant (cs) PCa detection. METHODS The study relied on the instructional retrospective biopsy data collected data between September 2013 and August 2017. Overall, 241 men with at least one suspicious lesion (PI-RADS ≥ 3) on multiparametric MRI underwent FBx. The study protocol contains a systematic 12-core sextant biopsy plus 2 cores per targeted lesion. One experienced urologist performed 1048 targeted biopsy cores; 467 (45%) cores were obtained using rigid processing, while the remaining 581 (55%) cores relied on elastic image processing. CsPCa was defined as International Society of Urological Pathology (ISUP) grade ≥ 2. The effect of rigid versus elastic FBx on overall and csPCa detection rates was determined. Propensity score weighting and multivariable regression models were used to account for potential biases inherent to the retrospective study design. RESULTS In multivariable regression analyses, age, prostate-specific antigen (PSA), and PIRADS ≥ 3 lesion were related to higher odds of finding csPCa. Elastic software-based image processing was independently associated with a higher overall PCa (odds ratio [OR] = 3.6 [2.2-6.1], p

PURPOSE To develop a deep-learning model that leverages the spatial and temporal information from dynamic contrast-enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling. METHODS Patients with glioblastoma multiforme were scanned post-resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence ( ≈5 s and ≈2 cm3 ). A region over the transverse sinus was manually drawn in the dynamic T1-weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep-learning model based on a 3D U-net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep-learning predicted regions and VF curves were compared. RESULTS Forty-three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U-net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves. CONCLUSION The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping.

Purpose Cone-beam CT (CBCT) has been widely utilized in image-guided radiotherapy. Planning CT (pCT)-aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT-based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF-RR). Methods First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter-free projections. The raw scatter signals were obtained via subtracting the scatter-free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF-RR method was evaluated via comparison with another pCT-based scatter correction method based on Median and Gaussian filters (MG method). Results Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG-RR), MG with deformable registration (MG-DR), and LF-RR reduced the CT number error from 79 ± 35 to 25 ± 18 , 17 ± 13 and 7 ± 3 HU for adipose and from 115 ± 61 to 36 ± 22 , 30 ± 24 , 7 ± 3 HU for muscle, respectively. After correction, the spatial non-uniformity (SNU) of CBCT corrected with MG-RR, MG-DR and LF-RR was 51 ± 13 , 60 ± 21 , and 21 ± 9 HU for adipose, and 50 ± 22 , 57 ± 41 , and 25 ± 6 HU for muscle, respectively. Meanwhile, the contrast-to-noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF-RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min. Conclusion We developed a fast and robust pCT-based CBCT scatter correction method which exploits the local-filtration technique to promote the accuracy of scatter estimation and is resistant to pCT-to-CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application.

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non-destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment.

Purpose To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping. Methods MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T1 /T2 /T1 ρ mapping in the brain was investigated. Results For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T1 /T2 /T1 ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T1 /T2 /T1ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T1 /T2 /T1 ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T1 /T2 /T1 ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance. Conclusion Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.

We describe the use of convolution neural networks to identify and quantify the silkworm eggs that are laid on a sheet of paper by female silk moth. The method is also capable of identifying individual egg and classifying them into Hatched egg class and Unhatched egg class, thus outperforming image processing tech-niques used earlier. We identify few limitations in the techniques employed earlier and attempted to increase accuracy using uniform illumination of a digital scan-ner. Use of digital scanner increases repeatability. Accordingly, we illustrate the use of standard key marker that helps to transform any silkworm egg sheet into a standard image, which can be used as input to a trained convolution neural net-work model to get predictions. We have also prepared silkworm datasets of over 100K images for each category that can be used to train CNN. The experimental results on test image sets show that our approach yields an accuracy of above 97% coupled with high repeatability.

Cell counting plays a vital role in biomedical researches. However, manual cell counting is time-consuming, laborious, and low efficiency and has a high counting error rate problem. An automatic counting approach for Hela cells of phase-contrast microscopic images is proposed based on the combination of Otsu and watershed segmentation methods to solve the mentioned issues. Firstly, image preprocessing is performed. Secondly, the Otsu method was used to obtain an automatic global optimal threshold for segmentation to achieve batch counting of images. Thirdly, the marker watershed was performed to separate adherent cells and to avoid over-segmentation simultaneously. Finally, cells in phase-contrast microscopic images were counted by detecting the numbers of connected domains in the binary image. Taking the manual counting result as the counting standard and MIS, INC, and ACC are used as evaluation indicators. The experimental results showed that the average values of MIS, INC, and ACC of the proposed method are only 3.31%, 3.49%, and 96.69%, respectively. Additionally, each cell image was counted only takes 0.65 s on averagely. To further test the performance of the proposed method, a comparative experiment was carried out by Image J, and the result shows that the proposed method has a better counting performance with a higher average accuracy of 96.55% to Image J with 93.39%.The proposed method for cell counting is simple, feasible, fast and high accurate, and it can be used as an effective method for cell counting of the phase-contrast microscopic images.

This paper introduces a new method to stylize 3D geometry. The key observation is that the surface normal is an effective instrument to capture different geometric styles. Centered around this observation, we cast stylization as a shape analogy problem, where the analogy relationship is defined on the surface normal. This formulation can deform a 3D shape into different styles within a single framework. One can plug-and-play different target styles by providing an exemplar shape or an energy-based style description (e.g., developable surfaces). Our surface stylization methodology enables Normal Captures as a geometric counterpart to material captures (MatCaps) used in rendering, and the prototypical concept of Spherical Shape Analogies as a geometric counterpart to image analogies in image processing.

Purpose The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction. Methods We used a pretrained deep neural network to represent PET images. The network was trained using low-count PET and CT image pairs as inputs and high-count PET images as labels. This network was then incorporated into a constrained maximum likelihood framework to regularize PET image reconstruction. Two different network structures were investigated for the integration of anatomical information from CT images. One was a multichannel CNN, which treated PET and CT volumes as separate channels of the input. The other one was multibranch CNN, which implemented separate encoders for PET and CT images to extract latent features and fed the combined latent features into a decoder. Using computer-based Monte Carlo simulations and two real patient datasets, the proposed method has been compared with existing methods, including the maximum likelihood expectation maximization (MLEM) reconstruction, a kernel-based reconstruction and a CNN-based deep penalty method with and without anatomical guidance. Results Reconstructed images showed that the proposed constrained ML reconstruction approach produced higher quality images than the competing methods. The tumors in the lung region have higher contrast in the proposed constrained ML reconstruction than in the CNN-based deep penalty reconstruction. The image quality was further improved by incorporating the anatomical information. Moreover, the liver standard deviation was lower in the proposed approach than all the competing methods at a matched lesion contrast. Conclusions The supervised co-learning strategy can improve the performance of constrained maximum likelihood reconstruction. Compared with existing techniques, the proposed method produced a better lesion contrast versus background standard deviation trade-off curve, which can potentially improve lesion detection.

Background Providing accurate 3-dimensional virtual bone surface models is a prerequisite for virtual surgical planning and additive manufacturing in craniomaxillofacial surgery. For this purpose, magnetic resonance imaging (MRI) may be a radiation-free alternative to computed tomography (CT) and cone beam computed tomography (CBCT). Purpose The aim of this study was to assess the geometric accuracy of 3-dimensional T1-weighted MRI-derived virtual bone surface models of the mandible in comparison to CT and CBCT. Materials and methods Specimens of the mandible from porcine cadavers were scanned with (1) a 3-dimensional T1-weighted MRI sequence (0.6 mm isotropic voxel) optimized for bone imaging, (2) CT, and (3) CBCT. Cortical mandibular structures (n = 10) were segmented using semiautomated and manual techniques. Imaging-based virtual 3-dimensional models were aligned with a high-resolution optical 3-dimensional surface scan of the dissected bone (=ground truth) and global geometric deviations were calculated (mean surface distance [MSD]/root-mean-square distance [RMSD]). Agreement between the imaging modalities was assessed by equivalence testing and Bland-Altman analysis. Results Intra- and inter-rater agreement was on a high level for all modalities. Global geometric deviations (MSD/RMSD) between optical scans and imaging modalities were 0.225 ± 0.020 mm/0.345 ± 0.074 mm for CT, 0.280 ± 0.067 mm/0.371 ± 0.074 mm for MRI, and 0.352 ± 0.076 mm/0.454 ± 0.071 mm for CBCT. All imaging modalities were statistically equivalent within an equivalence margin of ±0.3 mm, and Bland-Altman analysis indicated high agreement as well. Conclusions The results of this study indicate that the accuracy and reliability of MRI-derived virtual 3-dimensional bone surface models is equal to CT and CBCT. MRI may be considered as a reliable alternative to CT and CBCT in computer-assisted craniomaxillofacial surgery.

Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell-based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto-focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single-shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open-source Graphical User Interface called Bead Benchmarking-Focus And Intensity Tool (BB-FAIT) to implement these methods for a semi-automated cell viability analyser.It is critical for the manufacturing and release of living cell-based therapies to determine the viability, the ratio of living cells to the total number of cells (live and dead), in the therapy. Dead cells can be a safety concern for the patient, and dosing is often based on the number of living cells which are the active ingredient of the drug product. Currently, the most common approach to evaluating cell viability is based on the staining of cell samples with the trypan blue marker of cell membrane integrity: a loss in cell membrane integrity with cell death allows the dye into the cell, which can be seen using brightfield microscopy. To classify cells as live/dead, the brightness of the cells is evaluated and cells with bright centres are considered live, while those with dark centres are considered dead. Unfortunately, this approach of staining, imaging and classification is very sensitive to image acquisition settings, including image focus and brightness. This paper introduces a method to establish the required image quality for image viability analysis, providing a tool to return to image acquisition settings that will ensure image quality even when there is variability from sample to sample. In this method, polymeric beads are added to each cell sample prior to cell viability analysis. Using image processing, we extract key features from the beads in the image such as sharpness of the edges of the beads. The image features of the cells can vary significantly from sample to sample and under different cell conditions, but image features of beads have proved to be consistent across samples. We are thus able to collect reference datasets quantifying bead features over a wide range of image acquisition settings (brightness and focus), allowing us to establish a reference focal plan for image acquisition for any cell sample based on bead features. We show that with as few as three beads per image, the reference focal plane can be found from a single acquisition of beads image data over a wide range of image focuses and brightness, allowing users to consistently acquire images for cell viability that meet pre-defined quality requirements.

Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

Background Although many hair disorders can be readily diagnosed based on their clinical appearance, their progression and response to treatment are often difficult to monitor, particularly in quantitative terms. We introduce an innovative technique utilizing a smartphone and computerized image analysis to expeditiously and automatically measure and compute hair density and diameter in patients in real time. Methods A smartphone equipped with a dermatoscope lens wirelessly transmits trichoscopy images to a computer for image processing. A black-and-white binary mask image representing hair and skin is produced, and the hairs are thinned into single-pixel-thick fiber skeletons. Further analysis based on these fibers allows morphometric characteristics such as hair shaft number and diameters to be computed rapidly. The hair-bearing scalps of fifty participants were imaged to assess the precision of our automated smartphone-based device in comparison with a specialized trichometry device for hair shaft density and diameter measurement. The precision and operation time of our technique relative to manual trichometry, which is commonly used by hair disorder specialists, is determined. Results An equivalence test, based on two 1-sided t tests, demonstrates statistical equivalence in hair density and diameter values between this automated technique and manual trichometry within a 20% margin. On average, this technique actively required 24 seconds of the clinician's time whereas manual trichometry necessitated 9.2 minutes. Conclusion Automated smartphone-based trichometry is a rapid, precise, and clinically feasible technique which can significantly facilitate the assessment and monitoring of hair loss. Its use could be easily integrated into clinical practice to improve standard trichoscopy.

Optical coherence tomography (OCT) is an imaging modality that provides real-time visualization of tissue microstructure. The goals of this study were to assess OCT image tissue depths (TD) with image processing, and other objective characteristics of tissue types at surgical margins in canine soft tissue sarcoma (STS). In this study, a single observer reviewed 248 images of four tissue types (sarcoma, skeletal muscle, adipose and fascia) collected from 24 dogs with STS. The observer evaluated for tissue characteristics and measured TDs utilizing ImageJ software. Images were evaluated in normal, Threshold and Binary conditions. Measurements were repeated one week later to evaluate for intra-observer variability. In all three image processing conditions, the order of TD from greatest to least light penetration was adipose, skeletal muscle, fascia and sarcoma tissue. Neovascularization was observed in 53.2% sarcoma, and lines of fascia surrounding muscle bundles was present in 93.5% of skeletal muscle images. These observed differences between tissue types in OCT images can be utilized to improve observer evaluation, aid in development of algorithms, and improve the accuracy of surgical margin assessment. This article is protected by copyright. All rights reserved.

Even in these challenging times, cardboard packaging industry is increasing its revenue with stable annual increase prediction, but customers are increasing demands on the packaging with respect to environmental protection, attractiveness, and branding. This article aims to determine the influence of image processing in terms of gray component replacement and overprint varnishing on the color reproduction on a cardboard packaging material. A test chart with tertiary colors was defined and modified by different gray component replacement levels. It was printed in accordance with ISO norm on coated and uncoated cardboard and finished by overprinting water based , offset, and UV varnish. The prepared samples were evaluated by measuring surface coverage, tone value increase (dot gain) of primary colors and calculating dE00 of defined tertiary colors. The results showed that gray component replacement application reduces ink consumption. Higher gray component replacement applied results in lower ink surface coverage, but the decrease is also dependent on the image content. The TVI (dot gain) is significantly influenced only by applying UV varnish, resulting with higher TVI. The color differences of the tertiary colors showed that increased level of the gray component replacement applied did not cause significant color differences, even decreased color difference in some cases. Additionally, applying gray component replacement decreased color differences in “reddish” part of the gamut. This research proved that lowering the ink consumption due to the gray component replacement will not cause significant differences in the color reproduction. Further-more, varnishing with commercial offset or water-based varnish will not cause higher deviations in color reproduction, but use of the UV varnish would.

This study aimed to investigate the potential application of image texture processing method on visible crumb structure of salty cake pogacsa, which was prepared with different baking times (5 and 7 min) and temperatures (200, 215, and 230°C). For this purpose, changes in gray level co-occurrence matrix (GLCM) features including energy, contrast, correlation, homogeneity, and entropy were monitored and their relationship with the instrumental texture parameters (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness) were assessed. The pore ratios were also extracted and visualized using image processing technique. Texture profile parameters indicated strong correlation (p

In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time-lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well-known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label-free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell-cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.

Genetic Programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree-type structure. Similarly, research is also gaining momentum in the field of Image Processing, because of its promising results over vast areas of applications ranging from medical Image Processing to multispectral imaging. Image Processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, i.e., complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for Image Processing since its inception. Many interesting GP techniques have been developed and employed in the field of Image Processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in Image Processing and provides useful resources for further research. Also, the comparison of different parameters used in different applications of Image Processing is summarized in tabular form. Moreover, analysis of the different parameters used in Image Processing related tasks is carried-out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in Image Processing but also in other fields, may increase. Additionally, guidelines are provided for applying GP in Image Processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set., 31 pages, 12 figures, and 1 table

PURPOSE Channelized Hotelling observer (CHO) models have been implemented to assess imaging performance in x-ray angiography systems. While current methods are appropriate for assessing unprocessed images of moving test objects upon uniform-exposure backgrounds, they are inadequate for assessing conditions which more appropriately mimic clinical imaging conditions including the combination of moving test objects, complex anthropomorphic backgrounds, and image processing. In support of this broad goal, the purpose of this work was to develop theory and methods to automatically select a subset of task-specific efficient Gabor channels from a task-generic Gabor channel base set. Also, previously described theory and methods to manage detectability index (d') bias due to nonrandom temporal variations in image electronic noise will be revisited herein. METHODS Starting with a base set of 96 Gabor channels, backward elimination of channels was used to automatically identify an "efficient" channel subset which reduced the number of channels retained in the subset while maintaining the magnitude of the d' estimate. The concept of a pixelwise Hotelling observer (PHO) model was introduced and similarly implemented to assess the performance of the efficient-channel CHO model. Bias in d' estimates arising from temporally variable nonstationary noise was modeled as a bivariate probability density function for normal distributions, where one variable corresponds to the signal from the test object and the other variable corresponds to the signal from temporally variable nonstationary noise. Theory and methods were tested on uniform-exposure unprocessed angiography images with detector target dose (DTD) of 6, 18, and 120 nGy containing static disk-shaped test objects with diameter in the range of 0.5 to 4 mm. RESULTS Considering all DTD levels and test object sizes, the proposed method reduced the number of Gabor channels in the final subset by 63-82% compared to the original 96 Gabor channel base set, while maintaining a mean relative performance ( (dCHO'/dPHO')×100% ) of 95% ± 4% with respect to the reference PHO model. Experimental results demonstrated that the bivariate approach to account for bias due to temporally variable nonstationary noise resulted in improved correlation between the CHO and PHO models as compared to a previously proposed univariate approach. CONCLUSIONS Computationally efficient backward elimination can be used to select an efficient subset of Gabor channels from an initial channel base set without substantially compromising the magnitude of the d' estimate. Bias due to temporally variable nonstationary noise can be modeled through a bivariate approach leading to an improved unbiased estimate of d'.

The objective is to explore the appropriate method to establish the mathematical model of fascicular groups' contours from micro-CT images of peripheral nerves during the nonsplitting/merging phase. The original contours of fascicular groups from the micro-CT image were described as the discrete pixel points. All discrete pixel points of shapes were extracted into a data set through image processing. The data set was modeled by Bezier, B-spline method, respectively, in which each discrete point was used as a control point for modeling. In the Bezier method, the contour of a nerve bundle needs more than two different Bezier curves to model, making the junction points between two models discontinuous. The contour model described by B-spline is very close to the original contour of nerve bundles when all discrete points are used as the control points. The models described by B-spline have different terms and parameters, making it difficult to calculate in the following research. When the third-order quasi-uniform B-spline method is employed, all nerve bundles models have the same number of terms. The modeling error of third-order quasi-uniform B-spline is less than 3% when the Dice coefficient is more than 95%, and the appropriate number of sampling times is 21. The modeling accuracy is improved with increased sampling times when it is less than 21. However, the modeling accuracy remains stable while the number of sampling times is more than 21. The third-order quasi-uniform B-spline is more efficient in modeling nerve bundles' contour, which is more accurate and straightforward.

High-resolution microscopic imaging may cause intensive image processing and potential impact of light irradiation on yeast replicative lifespan (RLS). Electrical impedance spectroscopy (EIS) could be alternatively used to perform high-throughput and label-free yeast RLS assays. Prior to fabricating EIS-integrated microfluidic devices for yeast RLS determination, systematic modeling and theoretical investigation are crucial for device design and optimization. Here, we report three-dimensional (3D) finite-element modeling and simulations of EIS measurement in a microfluidic single yeast in situ impedance array (SYIIA), which is designed by patterning an electrode matrix underneath a cell-trapping array. SYIIA was instantiated and modeled as a 5 × 5 sensing array comprising 25 units for cell immobilization, culturing, and time-lapse EIS recording. Simulations of yeast growing and budding in a sensing unit demonstrated that EIS signals enable the characterization of cell growth and daughter-cell dissections. In the 5 × 5 sensing array, simulation results indicated that when monitoring a target cell, daughter dissections in its surrounding traps may induce variations of the recorded EIS signals, which could cause mistakes in identifying target daughter-cell dissections. To eliminate the mis-identifications, electrode array pitch was optimized. Therefore, the results could conduct the design and optimization of microfluidic electrode-array-integrated devices for high-throughput and accurate yeast RLS assays.

We investigate the atomic rearrangement in graphene under femtosecond pulse illumination with reactive molecular dynamics simulations and compare with ultra‐fast laser ablation experiments. To model the impact of the laser pulse irradiation, heat is locally applied to a selected area of the graphene layer and the resulting structural deformation is simulated as a function of time, providing a detailed understanding of the bond breaking process under laser illumination and subsequent re‐equilibration after the pulse is turned off. Analysis of the atomic dynamics indicates that the types of defects formed depend on the pulse energy and exposure duration. By varying the exposed area, we determine that the shape of the ablated area is not only a function of the pulse energy, but also of the beam spot size and pulse repetition. Furthermore, we apply a machine learning approach to extrapolate our simulated data to experimental length scales and reproduce the trends in ablated area as a function of temperature. Our study provides a first step towards understanding the design parameters for graphene nano‐patterning.

BACKGROUND: In archived diffusion tensor imaging (DTI) studies, a reversed-phase encoding (PE) scan required to correct the distortion in single-shot echo-planar imaging (EPI) may not have been acquired. Furthermore, DTI tractography is adversely affected by incorrect white matter segmentation due to leukoencephalopathy (LE). All these issues need to be addressed. PURPOSE: To propose and evaluate a modified DTI processing pipeline with DIstortion COrrection using pseudo T(2)-weighted images (DICOT) to overcome limitations in existing acquisition protocols. STUDY TYPE: Retrospective feasibility. SUBJECTS: DICOT was assessed in simulated data and 84 acute lymphoblastic leukemia (ALL) patients with reversed PE acquired. The pipeline was then tested in 522 scans from 261 ALL patients without a reversed PE acquired. FIELD STRENGTH/SEQUENCE: A 3 T; diffusion-weighted EPI; 3D magnetization prepared rapid acquisition gradient echo (MPRAGE). STATISTICAL TESTS: Repeated measures analysis of variance and Tukey post hoc tests were performed to compare fractional anisotropy (FA) values obtained by different methods. ASSESSMENT: FA and corresponding absolute error maps were obtained using TOPUP, DICOT, INVERSION (Inverse contrast Normalization for VERy Simple registratION) and NO CORR (no correction). Each method was assessed by comparing to TOPUP. The pipeline in the ALL patients was evaluated based on the failure rate of the distortion correction using the global correlation values. RESULTS: Using DICOT reduced the mean absolute errors by an average of 32% in FA in simulation datasets. In 84 patients, the error reductions were approximately 15% in FA with DICOT, while it was 5% with INVERSION. No significant differences between the TOPUP and DICOT were observed in FA with P = 0.090/0.894(AP/PA). Only 15 of 516 examinations requiring any additional manual intervention. CONCLUSION: This modified pipeline produced better results than the INVERSION. Furthermore, robust performance was demonstrated in archived patient scans acquired without an inverse PE necessary for TOPUP correction. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2

Multiparameter tissue imaging enables analysis of cell‐cell interactions in situ, the cellular basis for tissue structure, and novel cell types that are spatially restricted, giving clues to biological mechanisms behind tissue homeostasis and disease. Here, we streamlined and simplified the multiplexed imaging method CO‐Detection by indEXing (CODEX) by validating 58 unique oligonucleotide barcodes that can be conjugated to antibodies. We showed that barcoded antibodies retained their specificity for staining cognate targets in human tissue. Antibodies were visualized one at a time by adding a fluorescently labeled oligonucleotide complementary to oligonucleotide barcode, imaging, stripping, and repeating this cycle. With this we developed a panel of 46 antibodies that was used to stain five human lymphoid tissues: three tonsils, a spleen, and a LN. To analyze the data produced, an image processing and analysis pipeline was developed that enabled single‐cell analysis on the data, including unsupervised clustering, that revealed 31 cell types across all tissues. We compared cell‐type compositions within and directly surrounding follicles from the different lymphoid organs and evaluated cell‐cell density correlations. This sequential oligonucleotide exchange technique enables a facile imaging of tissues that leverages pre‐existing imaging infrastructure to decrease the barriers to broad use of multiplexed imaging., We simplified the multiplexed imaging method CO‐Detection by indEXing (CODEX) by employing cyclic stripping and annealing of fluorescent oligonucleotides to DNA‐barcoded antibodies. We stained five human lymphoid tissues with a panel of 46 antibodies. This enabled quantitative spatial single‐cell biology analysis of differential cell‐type composition of immune follicles.

Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) parametric maps, and the WM and GM probability maps. Results: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.