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To compare bone marrow fat quantification using magnetic resonance spectroscopy (MRS) and six-point DIXON (6PD) techniques in patients with sickle cell disease (SCD) and healthy subjects.Prospective study, with 43 SCD patients (24 homozygous [SS], 19 double heterozygous [SC), and 41 healthy subjects paired by age, weight and sex with SCD patients. All participants underwent magnetic resonance imaging with 6PD and single voxel MRS in the L3 vertebral body. Pearson's correlation, ROC curve, and bland-altman analysis were performed, p-values ​​≤0.05 were considered statistically significant for all tests.Significant linear correlation was found between fat fraction (FF) by 6PD and Total Lipids (TL) (r = 0.932; p0.001) and Saturated Lipids (SL) (r = 0.934; p0.001), in all subjects. Strong correlations were also identified considering subjects of the SS/SC subgroups. Despite high correlations, no significant difference was observed only between FF and SL in the SS subgroup (Bland-Altman analysis), indicating excellent agreement between the fat estimations in this specific situation. Significant differences were observed in all variables (FF, TL, SL) comparing the SCD and healthy subjects. The ROC curve between SCD and healthy subjects showed the following areas under the curve: FF(0.924)TL(0.883)SL(0.892).The comparison between fat quantification by the 6PD with MRS demonstrated an excellent correlation in SCD patients, especially in the SS subgroup, which usually has a higher degree of hemolysis. The diagnostic performance of 6PD and MRS is similar, with advantages of shorter imaging processing time and larger studied area with the 6PD.

PURPOSE To determine the relationship of drusen size as determined by spectral-domain optical coherence tomography (SD-OCT) with that measured on registered color fundus photography (CFP) images and to derive an OCT-based classification system that was comparable with that determined by CFP. METHODS Custom software was developed to register CFP images to the scanning laser ophthalmoscopy fundus images obtained simultaneously with the corresponding SD-OCT images, so that individual drusen observed on CFP could be matched with those seen on SD-OCT. Single druse size (diameter, area, volume, and height) on CFP and SD-OCT images from a phase two clinical trial was determined with the Duke OCT Retinal Analysis Program. RESULTS The sizes of 213 individual drusen were measured on CFP and SD-OCT. The drusen diameter measured on CFP was significantly correlated with those determined on SD-OCT (R: 0.879, P < 0.001). Based on the corresponding formula: drusen diameter on SD-OCT = 0.77 × (drusen diameter on CFP) + 50.67 µm, large drusen defined as ≥125 µm on CFP had a diameter ≥145 µm on OCT, medium drusen defined as 63 µm to 124 µm on CFP had diameters 100 µm to 144 µm on OCT, and small drusen defined as

Computer vision (CV) algorithms are widely utilized in imaging processing for medical and personal electronics applications. In sensorics CV can provide a great potential to quantitate chemosensors' signals. Here we wish to describe a method for the CV-assisted spectrofluorometer-free detection of common nitro-explosive components, e.g. 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT), by using polyaromatic hydrocarbon (PAH, PAH = 1-pyrenyl or 9-anthracenyl) – based bola-type chemosensors. The PAH components of these chemical bolas are able to form stable, bright emissive in a visual wavelength region excimers, which allows their use as extended matrices of the RGB colors after imaging and digital processing. In non-polar solvents, the excimers have poor chemosensing properties, while in aqueous solutions, due to the possible micellar formation, these excimers provide “turn-off” fluorescence detection of DNT and TNT in the sub-nanomolar concentrations. A combination of these PAH-based fluorescent chemosensors with the proposed CV-assisted algorithm offers a fast and convenient approach for on-site, real-time, multi-thread analyte detection without the use of fluorometers. Although we focus on the analysis of nitro-explosives, the presented method is a conceptual work describing a general use of CV for quantitative fluorescence detection of various analytes as a simpler alternative to spectrofluorometer-assisted methods.

In “anatomic” right hepatic trisectionectomy for advanced perihilar cholangiocarcinoma, the left hepatic duct is divided at the left side of the umbilical portion (UP) of the left portal vein (LPV). For this reason, the left hepatic duct is completely detached from the UP after all division of the portal branches arising cranially from the UP. However, little is known about these thin portal branches. Using 3D imaging processing software, we examined the portal branches arising cranially from the UP of the LPV in 100 patients who underwent multidetector row computed tomography (MDCT). Special attention was paid to the portal branch running to the left lateral sector, designated as the left cranio-lateral branch. The left cranio-lateral portal branch number was 0 in 57 patients, 1 in 32 patients, and 2 in 11 patients. Thus, 54 left cranio-lateral branches were identified, arising from near the cul-de-sac of the UP, from near the elbow of the LPV, or from the UP trunk. The median volume of the territory supplied by the left cranio-lateral portal branch was 21 mL (range, 5–47 mL), and the median ratio to the left lateral sector was 11.8% (range, 1.7–25.0%). Approximately 40% of patients had the left cranio-lateral portal branches arising cranially from the UP and running to the left lateral sector. When planning anatomic right hepatic trisectionectomy, the presence or absence of this branch should be checked by using 3D imaging with MDCT.

The inclusion of 18F-FDG PET as a biomarker in the diagnostic criteria of neurodegenerative diseases and its indication in the presurgical assessment for drug-resistant epilepsies allow to improve specificity of these diagnosis. The traditional interpretation of neurological PET studies has been performed qualitatively, although in the last decade, several quantitative evaluation methods have emerged. This technical development has become relevant in clinical practice, improving specificity, reproducibility and reducing the interrater reliability derived from visual analysis. In this article we update/review the main imaging processing techniques currently used. This may allow the Nuclear Medicine physician to know their advantages and disadvantages when including these procedures in daily clinical practice.

Artificial intelligence (AI) comprises a wide range of technologies and methods with heterogeneous degrees of complexity, applications, and abilities. In the cardiovascular field, AI holds the potential to fulfil many unsolved challenges, eventually translating into improved patient care. In particular, AI appears as the most promising tool to overcome the gap between ever-increasing data-rich technologies and their practical implementation in cardiovascular research, in the cardiologist routine, in the patient daily life and at the healthcare-policy level. A multiplicity of AI technologies is progressively pervading several aspects of precision cardiovascular medicine including early diagnosis, automated imaging processing and interpretation, disease sub-phenotyping, risk prediction and remote monitoring systems. Several methodological, logistical, educational, and ethical challenges are emerging by integrating AI systems at any stage of cardiovascular medicine. This review will discuss the basics of AI methods, the growing body of evidence supporting the role of AI in the cardiovascular field and the challenges to overcome for an effective AI-integrated cardiovascular medicine.

Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.

In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been traditionally based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are generated as a byproduct of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of imaging features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. It has satisfactory statistical and numerical properties. In the analysis of lung adenocarcinoma data, it identifies a heterogeneity structure significantly different from the alternatives and has satisfactory prediction and stability performance. This article is protected by copyright. All rights reserved.

Objective\ud Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position.\ud \ud Key Findings\ud AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent.\ud \ud Conclusion\ud This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients.\ud \ud Implications for practice\ud Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.

Wireless transmission capacity demands are continuously growing. Infrared optical wireless communication (IR-OWC) is considered as a promising solution for the future high-speed indoor applications because it can easily offer more than 10 GHz bandwidth with commercially available devices of the fiber-optic communication eco-systems. An IR-OWC cell employing narrow beam widths requires powerful beam-steering and localization functions to dynamically pinpoint the optical beams to different user devices (UDs), and typically has a limited service area. Powerful localization functions with high accuracy typically require sophisticated hardware and resource- and time-consuming signal processing, which may cause serious economical burdens to the communication system. Besides, in regard of a system with extended service area, it may be comprehensive and thus expensive to fully cover the whole space by densely spaced IR-OWC cells. In this article, we introduce a hybrid radio-optical wireless system, which offers high speed IR-OWC services at specific hotspots, and covers spatial gaps among the IR-OWC cells through broadcasting of Wi-Fi signals. This balances the requirements among capacity, coverage, and cost. For each IR-OWC cell, image feature matching based on processing with additional differential operations is proposed to more efficiently process the signals. The images are captured by IP cameras, and can be transparently transported over the communication system through Wi-Fi. Based on our proposal, a 10 Gb/s automatically beam-steered IR-OWC system integrated with ∼80 Mb/s WiFi and with localization function is designed and discussed. An independent test showing 0.24-cm localization accuracy for a 1-cm UD aperture over 180-cm distance is performed. Our processing time shows ∼50% efficiency improvement to the speeded up robust features (SURF) method, and has less recognition uncertainties.

Image processing has become a critical technology in a variety of science and engineering disciplines. While most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed. Here, we demonstrate optical analog imaging processing using flat optics including multi-layer architectures. The use of flat meta-optics opens new doors in optical image processing, such as edge imaging filters, as well as the freedom to spatially multiplex optical functions for off-loading processing tasks from the digital system.

In this work, we present the development of phantoms made out by anatomical models printed with a 3D printer and adequate them to fit an Ultra-Low Field Magnetic Resonance Imaging System, in order to obtain 2D images. It was used a machine learning algorithm to segment anatomical images from CT and MRI databases from three different organs, and therefore a 3D printer was used to build structures that can fit system’s bore and acquire images using basic acquisition sequences to obtain 2D images. The images obtained with the Ultra-Low Field MRI system, show the borders of the phantoms manufactured. This can help us to ensure that the contours from our phantoms can be appreciated with the imaging system, which can lead us to improve system’s hardware and imaging processing techniques for academic and research purposes.

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.

This paper demonstrates the first experiment of Gaofen-3 (GF-3) satellite operated in dual-channel sliding spotlight mode. First, the raw data of two channels need to be preprocessed, including channel error correction and signal reconstruction, so that it can be focused by the subsequent conventional single-channel sliding spotlight mode imaging methods. After imaging processing, to avoid the possible back-folded SAR image, it is necessary to carry out postprocessing step. The imaging result of the data acquired by GF-3 is first given in this paper, it proves the capacity of achieving high-resolution wide-swath of multi-channel sliding spotlight mode, and the effectiveness of the processing method.

Introduction Various embolization techniques are available for intracranial aneurysms. The volume of an aneurysm is of interest for the device selection and delivery of a variety of embolization techniques, such as: coils, liquid embolics, flow disruptors, and flow diverters. Accurate aneurysm sizing and volumetric information can help interventionalists assess flow and stability pre-treatment, and assess potential remnant or recanalization risks post-treatment. Materials and Methods This research project applies voxel-based volume calculations, from patient MRI medical imaging data, to determine accurate 3-D aneurysm volume calculations. Additionally, the application can display clinically relevant parameters, such as aneurysm neck diameter, dome height and midline-dome width (for dome: neck (D:N) ratio calculations. To develop the calculations, formalin-fixed canine aneurysms model samples are measured with a Bruker 7T® MRI and reconstructed in 3-D. (figure 1). The images is be processed using a MATLAB® algorithm. This algorithm patches together image segments from MRI and micro CT scans of animals and/or humans. The application acquires 3D MRI data, discretizes the domain intrasaccular aneurysm space, and evaluates the volume of the aneurysm sac. This data is compared to physical measurements of excised aneurysms and calibrated 2-D angiographic images. Results Imaging processing techniques may be used to determine the domain of aneurysms with unprecedented precision, with less than 5% volumetric error, whereas current measurement techniques, especially in 2-D planes have errors as high as 30%. This technique helps determine the macroscopic properties of aneurysms, as well as accurately calculate the volumes of heterogeneous features such as blebs or abnormal aneurysm shapes. Conclusion This project brings together clinical and engineering expertise to translate medical imaging data directly into volumetric measurements with highly precise calculations that are currently not available from 2-D angiographic images. Measuring the size and dimensional properties of aneurysms with voxel-based volume calculations provides a fast, reliable and repeatable resource for aneurysm assessment. This technique will help interventionalists appropriately asses risk and treatment options for a broad variety of aneurysm morphologies. Disclosures C. Settanni: None. T. Becker: 1; C; STTR (NIH). 2; C; Aneuvas. W. Merrit: 1; C; STTR(NIH). 2; C; Aneuvas.

Content based image retrieval (CBIR) has become an important factor in medical imaging research and is obtaining a great success. More applications still need to be developed to get more powerful systems for better image similarity matching, and as a result getting better image retrieval systems. This research focuses on implementing low-level descriptors to maximize the quality of the retrieval of medical images. Such a research is supposed to set a better result in terms of image similarity matching. In this research a system that uses low-level descriptors is introduced. Three descriptors have been developed and applied in an attempt to increase the accuracy of image matching. The final results showed a qualified system in medical images retrieval specially that the low-level image descriptors have not been used yet in the image similarity matching in the medical field.

Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge for which automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions for urban healthcare providers in the holoendemic malaria sub-Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub-Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state-of-the-art deep-learning object detectors requires the human-expert labor-intensive process of labelling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach to leverage routine clinical-microscopy labels from our quality-controlled malaria clinics to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/μl as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert-level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90 which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan, located within the most populous African country (Nigeria) and with one of the largest burdens of P. falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required. This article is protected by copyright. All rights reserved.

[EN] Mesial temporal sclerosis (MTS) is the principal cause of complex epilepsy, is manifested principally by gliosis and hippocampal volume loss. This project aims to develop an algorithm that allows automatic measurement of hippocampal volume and signal intensity in magnetic resonance imaging. The algorithm developed uses preprocessing of the images to reduce the artifacts and for the extraction of the features were used techniques of machine learning (support vector machine) and texture analysis. Results can help to optimize time in the assessment of the mesial temporal sclerosis and can contribute to the best training to the youngers neuroradiologists.

The analytical review and determining method of the morphology of structure inhomogeneities of selectively-permeable surface of polymer membranes are presented in this article. The aim of the work was the developing and research of automated method for calculating the morphology of microstructure inhomogeneities of selectively permeable surface of nanofiltration membranes OPMN-P and OFAM-K. The developed method of calculation allows to identify surface microstructural inhomogeneity of semi-permeable membranes and the contamination coefficient. The obtained data about the microstructural inhomogeneities and the ratio of clogging of the membranes allow for the actual conditions of the factory laboratories to predict and define the term of effective operation of nanofiltration by porous membranes OPMN-P and OFAM-K, which are equipped by roller elements of a baromembrane and electrobaromembrane plants for concentration and purification of technological solutions and wastewater in the electroplating, chemical and food industries. Implementation of this method is confirmed by developed software package for identification of surface microstructure inhomogeneities of semi-permeable membranes and the contamination coefficient. There is a methodology for automated calculation of the average diameter value of semi-permeable membranes clogging and the contamination coefficient of the porous objects in this paper. The software complex based on the proposed method is developed and allows to determine the microstructural inhomogeneity of the surface of the polymer membranes OPMN-P and OFAM-K No. 2018611402RU. The calculation method is based on the developed program that allows to study the description of the main functions of “imaging processing toolbox”. The use of modern tools to achieve the goal of this work is shown by using the capabilities of the software package Matlab 2017. Practically, the method is implemented on the example of the process of nanofiltration with the using of semi-permeable polymer membranes OPMN-P and OFAM-K.

Many industrial and transportation applications use combustion in dedicated chambers. Combustion implies, depending upon the nature and the amount of precursors, production of carbon dioxide, pollutants and dusts in terms of particulate matters. With the aim of reducing emission, lean combustion is of great interest. However flame stability within the combustor chamber is a key issue under lean conditions. In fact under lean conditions burners exhibit flame instability, flashback or lean blow out, until the flame extinction. Hence the online monitoring of these phenomena related to combustion instability is essential. One the most used techniques is to check temperature and flame stability by means of sensing probes resisting to high temperatures. Increasing the number of probes, it is possible to perform a 2D and 3D monitoring. However since these probes are costly and require heavy maintenance procedures, it could be wise to exploit imaging processing through cameras directed to portholes across which we can see inner parts, and atmosphere of the furnace/chamber. This paper illustrates findings related to monitoring the flame behaviour different operating conditions chamber by an advanced image processing. A specific algorithm has been developed to characterize the flame, hence, to perform measurements. Myriad filters have been utilized to enhance flame features.

Circular synthetic aperture radar (CSAR) is the imaging mode when the radar moves along a circular path and the observed area is always covered by the wave beam. It is different from traditional SAR modes (strip-map SAR and spotlight SAR) and has potential advantages such as 360° observation, target recognition, and three-dimensional reconstruction. According to the imaging processing of CSAR, motion error is an important issue affecting the CSAR image quality, but the motion compensatio n (MOCO) method for CSAR is underdeveloped. Accordingly, with detailed analysis the motion error model is established and a data-driven MOCO flow chart for CSAR is proposed. The real CSAR data are used to verify the proposed method.

We studied collagen fiber organization in tissue affected by pelvic organ prolapse (POP) and compared it to asymptomatic controls. Both the control and POP tissue biopsies were prepared and measured by a highly sensitive quantitative phase imaging (QPI) system, called spatial light interference microscopy (SLIM). Combined with automatic image processing, this modality provides quantitative, high-throughput assessment of fiber morphology. We found the fiber orientation in prolapsed specimens is less homogeneous, indicating an abnormal organization of collagen in the extracellular matrix (ECM).

This paper presents the first results of the combination of optical techniques, through fluorescence images captured by endoscopic techniques, and imaging processing methods to give a quantitative result that will be useful to physicians to observing and following up Photodynamic Therapy (PDT) for liver cancer. The experiment was applied to an animal model using δ-aminolevulinic acid (ALA) as a photosensitizing agent, red light for the application of PDT, white light for diffuse reflectance and light of 405 nm for the capture of fluorescence photographs with endoscopic procedures on the surface of the liver. As part of the image processing, metrics such as the Euclidean distance were used and the RGB color composition components.

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technique has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising in recent years. Moreover, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.

In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.

The compressed sensing (CS) based imaging methods for tomography SAR perform well in the case of large number of baselines. Unfortunately, for the current tomography SAR, the baselines are obtained from many multi-pass acquisitions on the same scene, which is expensive and can be severely affected by temporal decorrelation. In order to reduce the number of baselines, a novel strategy for tomography SAR imaging by introducing the block-sparsity theory into the imaging processing is proposed in this paper. Using neighboring pixels information in reconstruction, the proposed method can overcome the imaging quality limitation imposed by the low number of baselines. The results with simulation data under the additive gaussian noise case are presented to verify the effectiveness of the proposed method.

Employing a single-pixel digital holographic recording technique called optical scanning holography (OSH), we accomplish the formidable task of sectioning with edge extraction in three-dimensional (3D) optical incoherent imaging. OSH is a special variant of generalized two-pupil heterodyning image processing, where one of the pupils used is a delta function with the other being a uniform function. In this study, we investigate the use of an annular pupil and a random-phase pupil for edge extraction during sectioning of a 3-D object. Novel simulation results indicate excellent edge extraction of a multi-section object with good sectioning capability, i.e., with each focused edge-extracted section out-of-focused haze has been eliminated. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11762009, 61565010, 61865007]; Natural Science Foundation of Yunnan ProvinceNatural Science Foundation of Yunnan Province [2018FB101]; Key Program of Science and Technology of Yunnan Province [2019FA025]; General Research Fund (GRF) of Hong Kong SAR [9042822] National Natural Science Foundation of China (11762009, 61565010, 61865007); Natural Science Foundation of Yunnan Province (2018FB101); the Key Program of Science and Technology of Yunnan Province (2019FA025); the General Research Fund (GRF) of Hong Kong SAR (9042822).

Spiral phase contrast is an important and convenient imaging processing technology in edge detection, and a broader field-of-view (FOV) of imaging is a long-pursuing aim to see more regions of the illumination objects. Compared with near-infrared (NIR) spectrum, the up-conversion imaging in visible spectrum benefits from the advantages of higher efficiency detection and lower potential speckle. FOV enhanced and spiral phase contrast up-conversion imaging processing methods by using second order nonlinear frequency up-conversion from NIR spectrum to visible spectrum in two different configurations are presented in this work. By changing the temperature of crystal, controllable spatial patterns of imaging with more than 4.5 times enhancement of FOV is realized in both configurations. Additionally, we present numerical simulations of the phenomenon, which agree well with the experimental observations. Our results provide a very promising way in imaging processing, which may be widely used in biomedicine, remote sensing and up-conversion monitoring., 11 pages,5 figures,22 references

The purpose of this review is to bring the reader up to date on the current risk factors for the development of renal deterioration in the boys with posterior urethral valves (PUV) and approaches to modify this risk. Renal bladder ultrasound (RBUS) is routinely performed in boys with PUV and recent advancements allow imaging processing that can more accurately quantify renal parenchyma and correlate this with risk for renal loss. Refinement of urine studies may improve our ability to stratify patients into renal loss categories. Use of videourodynamics (VUDS) allows refined assessment of the valve bladder to identify those who might benefit from secondary procedures and/or the addition of targeted pharmacotherapy to improve bladder emptying or dangerous storage pressures. All boys with a history of PUV are at a significant long-term risk of renal deterioration. The literature suggests that several technical advances have improved our ability to predict this risk, although there needs to be further refinement and validation before widespread use. Utilization of close follow-up, VUDS, pharmacotherapy, and bladder drainage provide the best methods to improve care to this group of patients and if more studies confirm their utility, adoption of these as part of standard of care protocols may be warranted.

Rapid and accurate volumetric imaging remains a challenge, yet has the potential to enhance understanding of cell function. We developed and used a multifocal microscope (MFM) for 3D snapshot imaging to allow 3D tracking of insulin granules labeled with mCherry in MIN6 cells. MFM employs a special diffractive optical element (DOE) to simultaneously image multiple focal planes. This simultaneous acquisition of information determines the 3D location of single objects at a speed only limited by the array detector’s frame rate. We validated the accuracy of MFM imaging/tracking with fluorescence beads; the 3D positions and trajectories of single fluorescence beads can be determined accurately over a wide range of spatial and temporal scales. The 3D positions and trajectories of single insulin granules in a 3.2um deep volume were determined with imaging processing that combines 3D decovolution, shift correction, and finally tracking using the Imaris software package. We find that the motion of the granules is superdiffusive, but less so in 3D than 2D for cells grown on coverslip surfaces, suggesting an anisotropy in the cytoskeleton (e.g. microtubules and action).

Background Despite it is widely used in many medicine fields, the use of functional imaging to examine dental implants has not been reported in the literature. This work aimed to evaluate the relevance of functional medical imaging in oral implantology. Material and methods This single-center observational study was conducted for 6 months at the Toulouse University Hospital, France. All patients who underwent positron emission tomography with 18-fluorodeoxyglucose integrated with X-ray computed tomography (FDG PET/CT) and had dental implants were included. Metabolic activity of the peri-implant tissues was assessed qualitatively and quantitatively jointly by a nuclear physician and a dental surgeon. Results In 31 patients (121 implants), peri-implant metabolic activity was normal. In 3 patients (4 implants), localized peri-implant hypermetabolism was observed. In all the patients presenting abnormal peri-implant activity, the implants with normal activity were clinicaly and radiogicaly normal, whereas those with hypermetabolism presented peri-implantitis. Conclusions This study assess of the relevance of FDG PET/CT in oral implantology. It shows a link between peri-implant hypermetabolism and peri-implantitis. Therefore, FDG PET/CT could become a new tool for the assessment of peri-implant diseases. Key words:Dental implantation, dental implants, peri-implantitis, diagnostic imaging , imaging, three-dimensional, imaging processing, computer-assisted.

Background: Duchenne muscular dystrophy (DMD) is an X-linked lethal muscle disease. Dystrophic dogs are excellent models to test novel therapies for DMD. However, the use of the dog model has been hindered by the lack of an effective method to evaluate whole-body mobility. We recently showed that night activity is a good indicator of dog mobility. However, our published method relies on frame-by-frame manual processing of a 12-hour video for each dog. This labor-intensive and time-consuming approach makes it unrealistic to use this assay as a routine outcome measurement. Objective: To solve this problem, we developed an automatic video-capturing/imaging processing system. The new system reduces the data analysis time over 1,000 fold and also provides a more detailed activity profile of the dog. Methods: Using the new system, we analyzed more than120 twelve-hour recordings from 12 normal and 22 affected dogs. Results: We observed similar activity profiles during repeated recording of the same dog. Throughout the night, normal dogs were in motion 10.4 ± 0.9% of the time while affected dogs were in motion 4.6 ± 0.2% of the time (p < 0.0001). Further, normal dogs made significantly more movements (p < 0.0001) while affected dogs rested significantly longer (p < 0.0001) during the period of recording (from 6 pm to 6 am next day). Importantly, statistical significance persisted irrespective of the coat color, gender and mutation type. Conclusions: Our results suggest that night activity reduction is a robust, quantitative physiological biomarker for dystrophic dogs. The new system may be applicable to study mobility in other species.

A novel millimeter-wave imaging method suitable for fast personal screening is developed in this paper. The scanning process is formed by the 360° rotation of the array antennas, which could achieve high-resolution target profiles. To simplify the imaging processing procedure, the quasi-static approximation of the transceiver antennas is taken into account and the array configuration is optimized. Comprehensive simulations are carried out to demonstrate the good performance of the proposed scanning imaging method.

Down-looking synthetic aperture imaging ladar have bidirectional (positive and negative direction) modulation in the orthogonal direction of travel during phase modulation. The return signal can be also collected in the two directions. The imaging processing with bidirectional modulation is used and demonstrated. The signal-to-noise ratio can be enhanced in this mode synthetic aperture imaging ladar. Meanwhile, the velocity of carrying-platform can be faster. In the experiment, the return signals with bidirectional modulation are stacked and rebuilt. Compared to the unidirectional modulation imaging, the faster and clearer imaging is realized with bidirectional modulation. This technique has a great potential for applications in extensive synthetic aperture imaging ladar fields.

Milk, one of the important human foods, can not only be drunken directly but also be processed to produce more products. It is a critical issue for owners of ranches to effectively manage the pregnancy, reproduction, and milking of dairy cows. In current ranches, artificial insemination is performed to make dairy cows pregnant. Accurate estrus detection of dairy cows is essential for the success in artificial insemination. The traditional method to detect estrus of dairy cows is visual observation, whose success rate largely depends on the observers' experience and the frequency of observation. Therefore, several direct-contact monitor systems have been developed. But these sensors which directly contact the skin of dairy cows induce their discomfort. In this study, a non-contact video monitor system will be developed to avoid the contact-related discomfort. Infrared cameras will be used to record the imaging of dairy cows at night, when the frequency of estrus is the highest. Then the dairy cows with estrus-specific behaviors will be identified by imaging processing. Finally only simple judgments by owners are required to decide whether artificial insemination will be performed.

The staring array is the most popular design today. The major components are the optics, detector, imaging processing (discussed in Chapter 9, Image processing), flat-panel display, and the observer. Environmental effects include line-of-sight motion and atmospheric turbulence (discussed in Chapter 6, Environmental MTFs).

The focus of this research is the investigation of the Ocular Surface Temperature (OST) in both healthy and pathological (e.g. age-related macular degeneration, eye dry syndrome and choroidal lesions) eyes by using Infrared Thermography (IR). Specifically, the temperature of different anatomical areas (or points) of the eye is measured by applying image processing procedures on the infrared images recorded. It is known that the pathogenesis of some ocular pathology is related to the variation of ocular blood perfusion, and thus to the temperatures of the anterior segment of the eye. Results demonstrated that thermography can provide quantitative documentation of these temperature measurements. Furthermore, thermography is a non-invasive, cheap, harmless and portable technique. The challenge is to make ocular thermography part of the daily diagnostic procedure by providing more and more reliable experimental data.

This project is a smart wheelchair based on eye tracking which is designed for people with locomotor disabilities. The add-on controlled module can be used with any electrical wheelchair. The smart wheel chair consists of four modules including imaging processing module, wheelchair-controlled module, SMS manager module and appliance-controlled module. The image processing module comprises of a webcam installed on the eyeglass and C++ customized image processing software. The captured image which is transmitted to raspberry Pi microcontroller will be processed using OpenCV to derive the 2D direction of eye ball. The coordinate of eyeball movement is then wirelessly transmitted to wheelchair-controlled module to control the movement of wheel chair. The wheelchair-controlled module is two dimensional rotating stages that installed to the joystick of the electrical wheelchair to replace the manual control of the wheelchair. The motion of eyeball is also used as the cursor control on the raspberry Pi screen to control the operation of some equipped appliance and send message to smart phone.

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phase-unwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visible-light OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology.

This paper describes the development of new air-coupled ultrawideband ground-penetrating radar (GPR) for highway pavement and bridge deck inspections that can achieve high spatial resolution and high inspection performance while operating on vehicles driving at regularhighwayspeeds.Themajordesignfeaturesincludedual-frequencybandoperation,8gigasamplespersecondhigh-speedreal-time data acquisition,high-speedlargevolumedatatransmissionandstorage,andcustomizedsignal-processingalgorithmsforGPRimageenhancement and feature extractions. For design validations, steel reinforcing bar detection experiments in various setups are conducted. The measurement results show good agreement with the subsurface feature configurations. DOI: 10.1061/(ASCE)EM.1943-7889.0000458. © 2013 American Society of Civil Engineers. CE Database subject headings: Structure reinforcement; Sampling; Imaging techniques; Radar. Author keywords: GPR system; Rebar detection; High-speed real-time sampling; Air coupled; Imaging processing.

With the rapid development of the one-stationary bistatic forward-looking synthetic aperture radar (OS-BFSAR) technology, the huge amount of the remote sensing data presents challenges for real-time imaging processing. In this paper, an efficient time-domain algorithm (ETDA) considering the motion errors for the OS-BFSAR imaging processing, is presented. This method can not only precisely handle the large spatial variances, serious range-azimuth coupling and motion errors, but can also greatly improve the imaging efficiency compared with the direct time-domain algorithm (DTDA). Besides, it represents the subimages on polar grids in the ground plane instead of the slant-range plane, and derives the sampling requirements considering motion errors for the polar grids to offer a near-optimum tradeoff between the imaging precision and efficiency. First, OS-BFSAR imaging geometry is built, and the DTDA for the OS-BFSAR imaging is provided. Second, the polar grids of subimages are defined, and the subaperture imaging in the ETDA is derived. The sampling requirements for polar grids are derived from the point of view of the bandwidth. Finally, the implementation and computational load of the proposed ETDA are analyzed. Experimental results based on simulated and measured data validate that the proposed ETDA outperforms the DTDA in terms of the efficiency improvement.

Context: Mobile applications support a set of user-interaction features that are inde- pendent of the application logic. Rotating the device, scrolling, or zooming are examples of such features. Some bugs in mobile applications can be attributed to user-interaction features. Objective: This paper proposes and evaluates a bug analyzer based on user- interaction features that uses digital image processing to find bugs. Method: Our bug analyzer detects bugs by comparing the similarity between images taken before and after a user-interaction. SURF, an interest point detector and descriptor, is used to compare the images. To evaluate the bug analyzer, we conducted a case study with 15 randomly selected mobile applications. First, we identified user-interaction bugs by manually testing the applications. Images were captured before and after applying each user-interaction feature. Then, image pairs were processed with SURF to obtain interest points, from which a similarity percentage was computed, to finally decide whether there was a bug. Results: We performed a total of 49 user-interaction feature tests. When manually testing the applications, 17 bugs were found, whereas when using image processing, 15 bugs were detected. Conclusions: 8 out of 15 mobile applications tested had bugs associated to user-interaction features. Our bug analyzer based on image processing was able to detect 88% (15 out of 17) of the user-interaction bugs found with manual testing.

The growing number of different models and approaches for Geographic Information Systems (GIS) brings high complexity when we want to develop new approaches and compare a new GIS algorithm. In order to test and compare different processing models and approaches, in a simple way, we identified the need of defining uniform testing methods, able to compare processing algorithms in terms of performance and accuracy regarding: large imaging processing, algorithms for GIS pattern-detection.

Endovascular aortic aneurysm repair (EVAR) is a predominant surgical procedure to reduce the risk of aneurysm rupture in abdominal aortic aneurysm (AAA) patients. Endoleak formation, which eventually requires additional surgical reoperation, is a major EVAR complication. Understanding the etiology and evolution of endoleak from the hemodynamic perspective is crucial to advancing the current posttreatments for AAA patients who underwent EVAR. Therefore, a comprehensive flow assessment was performed to investigate the relationship between endoleak and its surrounding pathological flow fields through computational fluid dynamics and image processing. Six patient-specific models were reconstructed, and the associated hemodynamics in these models was quantified three-dimensionally to calculate wall stress. To provide a high degree of clinical relevance, the mechanical stress distribution calculated from the models was compared with the endoleak positions identified from the computed tomography images of patients through a series of imaging processing methods. An endoleak possibly forms in a location with high local wall stress. An improved stent graft (SG) structure is conceived accordingly by increasing the mechanical strength of the SG at peak wall stress locations. The presented analytical paradigm, as well as numerical analysis using patient-specific models, may be extended to other common human cardiovascular surgeries.

The use of ultrasound equipment in routine clinical evaluation of the gastrointestinal tract is still standby. Objective: to show an alternative application of ultrasound technique for monitoring peristalsis and gastric emptying through an executable routine implemented in LabVIEW. Methods: an ultrasound equipment (Medison SONACE 800 SE) was used in ten healthy subjects and four patients; dominant peristalsis frequency and gastric emptying were measured after fasting nights of the patients; subjects ingested, first 300 mL of water and then a solid meal (240 kCal). Results: the video acquisition in M mode and imaging processing were performed in a LabVIEW platform, as long as digital signal processing was carried out in matlab 2008a; peristaltic frequency of 2.4 cpm (cycles per minute) and a half time of the gastric emptying around 35 s were found. Discussion: these findings are congruent with similar evaluations of GI tract obtained by using the gold standard technique. So, ultrasound technique is now an alternative procedure for gastric evaluation with the advantage that patients are free of ionizing radiation, and these equipments are amply available in the world hospitals.

Armin Wolf,1,* Burkhardvon Jagow,2,* Daniel Kook,1 Elisabeth M Messmer,1 Carlo A Lackerbauer,1 Anselm Kampik,1 Thomas Kohnen,2 Martin Grueterich1 1Department of Ophthalmology, Ludwig-Maximilians-Universität, München, 2Department of Ophthalmology, Johann Wolfgang Goethe-Universität, Frankfurt am Main, Germany *These authors have contributed equallyBackground: With increasing numbers of lamellar keratoplasties, eye banks are challenged to deliver precut lamellar donor tissue. In Europe, the most common technique of corneal storage is organ culture which requires a deswelling process before surgical processing. The aim of this study was to investigate the influence of different deswelling times on the cutting plane quality after microkeratome-assisted lamellar dissection.Methods: Eight paired donor corneas (16 specimens) not suitable for transplantation were organ cultured under standard conditions at the Eye Bank of the Ludwig-Maximilians Universität, Munich, Germany. Pairs of corneal buttons were analyzed during the deswelling process in dextrane-containing medium. While one cornea was cut at an early time point during the deswelling process and put back into deswelling medium thereafter, the partner cornea was completely deswollen and dissected after 72 hours. Specimens were then further processed for scanning electron microscopy. Surface quality was assessed both digitally using Scanning Probe Imaging Processing software, and manually by three blinded graders.Results: The corneal buttons processed at the beginning of the deswelling process had a smoother surface when compared to the partner cornea that was cut at the end of the deswelling process. In our setting, no relevant difference was detectable between manual and automated microkeratome dissection.Conclusion: For lamellar keratoplasty, organ-cultured corneas should be processed at an early stage during the deswelling process. We interpret the smoother dissection plane during early deswelling as a result of mechanical properties in a highly hydrated cornea.Keywords: keratoplasty, deswelling, corneas, microkeratome