Image Acquisition Process Research Articles

SAR Image Registration in the Presence of Rotation and Translation: A Constrained Least Squares Approach

This letter proposes a coregistration algorithm to compensate for the possible inaccuracy of trajectory sensor during the synthetic aperture radar (SAR) image acquisition process. Such a misalignment can be modeled as a pure displacement in range and azimuth directions and a rotation effect due to different angles of sight. The approach is formalized as a constrained least squares (CLS) optimization problem enforcing a constraint of the absence of a zooming effect between the two SAR images. Moreover, the system equations can optionally be weighted according to local properties between the extracted patches within the quoted couple. Interestingly, the solution can be obtained in closed form, therefore, with a low computational cost. The results of the tests conducted on the 9.6-GHz Gotcha SAR data demonstrate the capability of the strategy to properly register the imagery.

A Smartphone Integrated Hand-Held Gap and Flush Measurement System for in Line Quality Control of Car Body Assembly.

This paper presents the design and the characterization of a portable laser triangulation measurement system for measuring gap and flush in the car body assembly process. Targeting Human in the Loop (HILT) operations in the manufacturing sector, and in line with the vision of human empowerment with Industry 4.0 technologies, the instrument embeds features to ease operators’ activity and compensate possible misuse that could affect the robustness and the quality of data acquired. The device is based on a smartphone integrated with a miniaturized laser triangulation system installed in a cover. The device embodies additional sensors and control systems in order to guarantee operators’ safety (switching on and off the laser line based on specific conditions), support operators during the measurement execution task, and optimize the image acquisition process for minimizing the uncertainty associated to the measurement. The smartphone performs on-board processing and allows Wi-Fi communication with the plant IT infrastructure. Compliance to Industry 4.0 requirements is guaranteed using OPC-UA (Open Platform Communications—Unified Architecture) communication protocol enabling the exchange of live data with the plant middleware. The smartphone provides also an advanced high-resolution color display and well proven and ergonomic human–machine interfaces, which have been fully exploited in the design. The paper introduces the system optical layout and then presents the algorithms implemented to realize the gap and flush measurement. The paper finally presents the calibration of the instrument and estimates its calibration uncertainty in laboratory conditions. Then it discusses how performance decays when the operator handles the instrument on a reference car body. Finally, it shows the analysis of uncertainty when the device is used on real car bodies of different colors in a production line. It is observed that the measurement uncertainty of the whole measurement chain (measurand + instrument + operator + uncontrolled environmental conditions) is larger than the instrument calibration uncertainty because the measurement process is affected by the operator and the variable conditions of the production line.

Radiomics in Cardiac MRI: Sisyphean Struggle or Close to the Summit of Olympus?

Radiomics in Cardiac MRI: Sisyphean Struggle or Close to the Summit of Olympus?

Phantom design for analysis of CT image quality from Single-source and Dual-source CT scan

Computed Tomography (CT) Scanner is a three-dimensional body imaging modality. For observation of moving organs, in addition to using Single-source CT (SSCT), Dual-source CT (DSCT) has been developed which is equipped with primary and secondary x-ray sources. The position of the two DSCT sources forms an angle of 90°, and at the time of image acquisition both rotate 90°. Thus the process of image acquisition of a moving body can be carried out in a relatively short time. If the same examination using SSCT, it is necessary to decrease the movement of organs which generally use beta blockers. In this research, in-house phantom has been designed to analyze the image quality character of the two types of CT which will then be compared. Phantom is cylindrical in shape and has a diameter of 20 cm and a length of 12 cm, divided into three sections of 4 cm each. The first module, phantom is intended for noise measurement, in the phantom implanted 16 cylindrical objects with diameters of 10 mm and 15 mm, made of 4 types of material with different Hounsfield Unit (HU) values, namely teflon (941.31 ± 14.61) HU, water (0.548 ± 0.43) HU, polystyrene (-26.38 ± 13.55) HU, and air (984.32 ± 27.97) HU. The second module, phantom will be used to assess the detectability. This section is filled with 26 cylindrical pipes with diameters, 2, 3, 4, 5, 6 mm for each of the 5 pipes and one other pipe with a diameter of 10 mm. All of these pipes will be filled with contrast media Iodine with varying concentrations. The third module, phantom will be used to measure spatial resolution and homogeneity. In this section copper ball objects will be implanted with a diameter of 0.19 mm and a length of 4 cm. Furthermore phantom will be used for measurement of noise, spatial resolution and detectability of this CT image from dual-source and single-source CT scans with the exposure conditions adjusted for clinical use.

Initial data from an experiment to implement a safe procedure to perform PA erect chest radiographs for COVID-19 patients with a mobile radiographic system in a “clean” zone of the hospital ward

IntroductionWith the current Covid-19 pandemic, general wards have been converted into cohort wards for Covid-19 patients who are stable and ambulant. A 2-radiographer mobile radiography team is required to perform bedside Chest X-rays (CXR) for these patients. Hospital guidelines require both radiographers to be in full Personal Protective Equipment (PPE) throughout the image acquisition process and the mobile radiographic unit needs to be disinfected twice after each case. This affects the efficiency of the procedure and an increase usage of limited PPE resources. This study aims to explore the feasibility of performing mobile chest radiography with the mobile radiographic unit in a “clean” zone of the hospital ward. MethodsAn anthropomorphic body phantom was used during the test. With the mobile radiographic unit placed in a “clean” zone, the phantom and the mobile radiographic unit was segregated by the room door with a clear glass panel. The test was carried out with the room door open and closed. Integrated radiation level and patient dose were measured. A consultant radiologist was invited to review and score all the images acquired using a Barco Medical Grade workstation. The Absolute Visual Grading Analysis (VGA) scoring system was used to score these images. ResultsA VGA score of 4 was given to all the 40 test images, suggesting that there is no significant differences in the image quality of the images acquired using the 2 different methods. Radiation exposure received by the patient at the highest kV setting through the glass is comparable to the regular CXR on patient without glass panel at 90 kV, suggesting that there is no significant increase in patient dose. ConclusionThe result suggests that acquiring CXR with the X-ray beam attenuating through a glass panel is a safe and feasible way of performing CXR for COVID-19 patients in the newly converted COVID wards. This will allow the mobile radiographic unit as well as one radiographer to be completely segregated from the patient. Implications for practiceThis new method of acquiring CXR in an isolation facility set up requires a 2-Radiographer mobile radiography team, and is applicable only for patients who are generally well and not presented with any mobility issues. It is also important to note that a clear glass panel must be present in the barriers set up for segregation between the “clean” zone and patient zone in order to use this new method of acquiring CXR.

Single-pixel sensing with optimal binarized modulation

Conventional high-level sensing techniques require high-fidelity images as input to extract target features. The images are produced by either complex imaging hardware or high-complexity reconstruction algorithms. In this Letter, we propose single-pixel sensing (SPS) that performs high-level sensing directly from a small amount of coupled single-pixel measurements, without the conventional image acquisition and reconstruction process. The technique consists of three steps, including binarized light modulation at ∼22.7kHz refresh rate, single-pixel coupled detection with a wide working spectrum and high signal-to-noise ratio, and end-to-end deep-learning-based decoding that reduces both hardware and software complexity. Also, the binarized modulation patterns are optimized with the decoding network by a two-step training strategy, leading to the least required measurements and optimal sensing accuracy. The effectiveness of SPS is experimentally demonstrated on the classification task of the handwritten MNIST dataset, and 96% classification accuracy at ∼1kHz is achieved. The reported SPS technique is a novel framework for efficient machine intelligence, with data-reduced acquisition and load-relieved processing.

Possible new options and benefits to detect myocarditis, right ventricular remodeling and coronary anomalies by echocardiography in systematic preparticipation screening of athletes

Exclusion of cardiac abnormalities should be performed at the beginning of the athlete’s career. Myocarditis, right ventricular remodeling and coronary anomalies are well-known causes of life-threatening events of athletes, major cardiovascular events and sudden cardiac death. The feasibility of an extended comprehensive echocardiographic protocol for the detection of structural cardiac abnormalities in athletes should be tested. This standardized protocol of transthoracic echocardiography includes two- and three-dimensional imaging, tissue Doppler imaging, and coronary artery scanning. Post processing was performed for deformation analysis of all compounds including layer strain. During 2017 and 2018, the feasibility of successful image acquisition and post processing analysis was retrospectively analyzed in 54 male elite athletes. In addition, noticeable findings inside the analyzed cohort are described. The extended image acquisition and data analyzing was feasible from 74 to 100%, depending on the used modalities. One case of myocarditis was detected in the present cohort. Coronary anomalies were not found. Right ventricular size and function were within normal ranges. Isovolumetric right ventricular relaxation time showed significant regional differences. One case of hypertrophic cardiomyopathy and two subjects with bicuspid aortic valves were found. Due to the excessive cardiac stress in highly competitive sports, high-quality and precise screening modalities are necessary, especially with respect to acquired cardiac diseases like acute myocarditis and pathological changes of left ventricular and RV geometry. The documented feasibility of the proposed extended protocol underlines the suitability to detect distinct morphological and functional cardiac alterations and documents the potential added value of a comprehensive echocardiography.

Failure and wear analysis of poly(butylene terephthalate) nanocomposite spur gears

This paper investigates the wear and failure mechanisms in poly(butylene terephthalate) (PBT) nanocomposite gear pairs. An image-based technique was employed to measure the gear tooth wear. Image acquisition process was further used to identify tooth wear patterns and explore gear failure mechanisms. Gear test results revealed that the inclusion of nanofillers increased the load-bearing capacity and wear resistance. Gear failure mode was altered from thermal tooth deflection for PBT to dominant crack-induced tooth fracture for the nanocomposites. Morphology analysis of the worn teeth indicated the transition of wear mechanism from adhesive for pure PBT to abrasive type with the inclusion of nanofillers. Furthermore, the strain field on the gear teeth was analyzed by applying digital image correlation (DIC) technique.

CNN based lane detection with instance segmentation in edge-cloud computing

At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation.

Optical coherence tomography (OCT) is a rapidly developing non-invasive three dimensional imaging approach, and it has been widely used in examination and diagnosis of eye diseases. However, speckle noise are often inherited from image acquisition process, and may obscure the anatomical structure, such as the retinal layers. In this paper, we propose a novel method to reduce the speckle noise in 3D OCT scans, by introducing a new super-resolution approach. It uses a multi-frame fusion mechanism that merges multiple scans for the same scene, and utilizes the movements of sub-pixels to recover missing signals in one pixel, which significantly improves the image quality. To evaluate the effectiveness of the proposed speckle noise reduction method, we have applied it for the application of retinal layer segmentation. Results show that the proposed method has produced promising enhancement performance, and enable deep learning-based methods to obtain more accurate retinal layer segmentation results.

Image Distortion in Biplanar Slot Scanning: Part 2 Technology-specific Factors.

Biplanar digital slot scanning technology has become the standard of care in the treatment of scoliosis. Yet, the amount of distortion and reproducibility of this type of imaging modality has yet to be fully investigated. In our paper "Image distortion in biplanar slot scanning: part 1 patient-specific factors" we found that there was potentially clinically impactful interimage distortion. The purpose of this study was to evaluate the degree to which this image distortion was secondary to the image acquisition process. Four 25 mm radio-opaque markers were placed at C3, T1, T12, and L5 on a full-length skeleton model. The skeleton was imaged in 10 different positions within the scanner. Five posteroanterior and 5 lateral images were obtained in each position. Two orthopaedic attending physicians and 3 orthopaedic resident physicians measured the markers for a total of 3200 measurements. Intraclass correlation coefficients (ICCs) and 95% confidence intervals were used to examine image distortion. Average marker size was 24.77, with a standard error of measurement of 0.00493. Image distortion and standard error of measurement accounted for ∼0.5% to 1.5% of total the measurement. Overall, there was good reliability and consistency when looking at markers in different views (ICC 0.790), planes, and locations within the image. Horizontal measurements were found to be more consistent and have better reliability (ICC 0.881) than vertical measurements (ICC 0.386). Position within the scanner had minimal impact on the accuracy of the measurements. This study demonstrates that there is minimal error due to image acquisition and measurement when using a biplanar slot scanner. Biplanar slot scanning technology tended to underestimate the size of the marker; however, the least accurate measurements only erred by 1.5% from the true length. This indicates that unlike traditional radiographs the sources of error in biplanar slot scanning images are not due to parallax and are likely due to patient-specific factors and rather than the technology itself.

Effective microtissue RNA extraction coupled with Smart-seq2 for reproducible and robust spatial transcriptome analysis

Spatial transcriptomics is useful for understanding the molecular organization of a tissue and providing insights into cellular function in a morphological context. In order to obtain reproducible results in spatial transcriptomics, we have to maintain tissue morphology and RNA molecule stability during the image acquisition and biomolecule collection processes. Here, we developed a tissue processing method for robust and reproducible RNA-seq from tissue microdissection samples. In this method, we suppressed RNA degradation in fresh-frozen tissue specimens by dehydration fixation and effectively collected a small amount of RNA molecules from microdissection samples by magnetic beads. We demonstrated the spatial transcriptome analysis of the mouse liver and brain in serial microdissection samples (100 μm in a diameter and 10 μm in thickness) produced by a microdissection punching system. Using our method, we could prevent RNA degradation at room temperature and effectively produce a sequencing library with Smart-seq2. This resulted in reproducible sequence read mapping in exon regions and the detection of more than 2000 genes compared to non-fixed samples in the RNA-seq analysis. Our method would be applied to various transcriptome analyses, providing the information for region specific gene expression in tissue specimens.

Kidney segmentation in MR images using active contour model driven by fractional-based energy minimization

In the field of diagnosis and treatment planning of kidney-related diseases, accurate kidney segmentation is challenging due to intensity inhomogeneity caused by imperfections during image acquisition process. This study presents a model, which consists of a novel fractional energy minimization for segmenting human kidney organ from MR images. Unlike existing active contour models (Chan–Vese), which uses gradient-based energy minimization that is sensitive to inhomogeneous intensity values, we propose a novel fractional Mittag–Leffler’s function for energy minimization, a technique more suitable to cope with the mentioned challenges. The proposed model exploits the special property of fractional calculus in maintaining high-frequency contour features while enhancing the low-frequency detail of texture in smooth area. Experimental results performed on complex kidney images using the proposed method show that the proposed model outperforms the existing models in terms of sensitivity, accuracy, Jaccard index and Dice coefficient.

Transport of intensity equation method based on edge detection and duty ratio fusion

The non-interferometric phase recovery method which is based on the transport of intensity equation is an important phase acquisition technique in microscopy imaging. In the actual operational process of this technology, at least three intensity images are needed to solve the equation. In the image acquisition process, the selection of the focused image is very important, but it is usually determined by subjective methods, thus causing inaccurate positioning of the in-focus, which further affects the precision of phase recovery result. A phase recovery algorithm based on edge detection and duty ratio is proposed. Firstly, a series of ‘subjectively determined focused images’ collected by the microscope are edge-extracted to obtain corresponding edge information points. Then the real in-focus position is found by calculating the duty ratio, and the corresponding defocused images are selected according to the position of the focused image. Finally, the high-precision phase recovery result is obtained by using these images of the accurate position to solve the equation. The algorithm does not require complex operations such as hardware modifications and can be applied to both discrete and continuous sample distributions. The experimental results effectively prove that the algorithm can accurately find the in-focus position, which improves the accuracy of phase recovery compared with the traditional method that is subjectively determined in-focus image position.

Determining uranium ore concentrates and their calcination products via image classification of multiple magnifications

Many tools, such as mass spectrometry, X-ray diffraction, X-ray fluorescence, ion chromatography, etc., are currently available to scientists investigating interdicted nuclear material. These tools provide an analysis of physical, chemical, or isotopic characteristics of the seized material to identify its origin. In this study, a novel technique that characterizes physical attributes is proposed to provide insight into the processing route of unknown uranium ore concentrates (UOCs) and their calcination products. In particular, this study focuses on the characteristics of the surface structure captured in scanning electron microscopy (SEM) images at different magnification levels. Twelve common commercial processing routes of UOCs and their calcination products are investigated. Multiple-input single-output (MISO) convolution neural networks (CNNs) are implemented to differentiate the processing routes. The proposed technique can determine the processing route of a given sample in under a second running on a graphics processing unit (GPU) with an accuracy of more than 95%. The accuracy and speed of this proposed technique enable nuclear scientists to provide the preliminary identification results of interdicted material in a short time period. Furthermore, this proposed technique uses a predetermined set of magnifications, which in turn eliminates the human bias in selecting the magnification during the image acquisition process.

Optical coherence tomography angiography (OCTA) applications in ocular oncology.

Optical coherence tomography angiography (OCTA) is a revolutionary method in the visualization of the vascular system in different retinal and choroidal layers. During the last 4 years since the commercial availability of different OCTA devices, attempts have been made to utilize this technology in various aspects of ocular oncology from the differentiation of benign and malignant lesions to assisting in evaluation of post-treatment complications, such as radiation retinopathy. However, current OCTA technology is restricted by various artefacts and inherent limitations, some of which are more pronounced in the presence of elevated tumoural lesions. Imminent advancements in OCTA systems and image acquisition processes promise a great potential for application of OCTA in ocular oncology.

A secure image permutation–substitution framework based on chaos and compressive sensing

Existing secure image acquisition works based on compressive sensing, viewing compressive sensing–based imaging system as a symmetric cryptosystem, can only achieve asymptotic spherical security denoting that the ciphertext only leaks information about the energy of plaintext. Thereby, compressive sensing–based secure image acquisition systems usually work in some scenarios of friendly attack, such as brute-force attack for the entire key space and ciphertext-only attack for the complete plaintext. In this article, a novel permutation–confusion strategy using chaos and compressive sensing is put forward to protect the privacy-crucial images from more threatening malicious attacks, including known-plaintext attack and chosen-plaintext attack. Security guarantee is provided by one-time random projection of compressive sensing, sample-level permutation, and bit-level substitution. It is noteworthy that double confidentiality is embedded in compressive sensing–based image acquisition process using the key-related measurement matrix and sparsifying basis and the randomicity is improved. Simulation experiments and security analyses suggest that the proposed framework can achieve a high-level security and the performance of image reconstruction can be distinctly improved.

BRAÇO ROBÓTICO PARA POSICIONAMENTO DE UM SISTEMA DE VISÃO ARTIFICIAL PARA MONITORAR DESGASTE DE FERRAMENTA DE USINAGEM DE UM TORNO CONVENCIONAL

O estado de desgaste e avarias da ferramenta de corte (inserto) durante o processo de usinagem é um dos fatores que influenciam diretamente na fabricação de uma peça e nas tolerâncias dimensionais e acabamento da mesma. O monitoramento do inserto pode evitar a perda de peças e a reposição do mesmo, além de evitar a troca antes da real necessidade. Deste modo, o objetivo foi construir um braço robótico para automatizar o processo de aquisição de imagens do inserto de corte em um torno mecânico. Para isso foi desenvolvido um manipulador robótico que de forma automática adquire imagem do inserto no torno e analisa a integridade da mesma com o auxílio de um software. Nesse trabalho foi desenvolvida a parte mecânica, eletrônica e de programação do manipulador robótico de modo que ele seja capaz de posicionar o microscópio digital para aquisição de imagens com alta repetitividade. Por fim, foi necessário acresentar um sistema de malha fechada que garantiu alta acurácia e precisão do sistema.

Enzyme-based detection of epoxides using colorimetric assay integrated with smartphone imaging.

Epoxides are widely used chemicals, the determination of which is of paramount importance. Herein, we present an enzyme-based approach for noninstrumental detection of epoxides in standard solution and environmental samples. Halohydrin dehalogenase (HheC) as a biological recognition element and epichlorohydrin as a model analyte were evaluated for sensing. The detection is based on the color change of the pH indicator dye bromothymol blue caused by the HheC-catalyzed ring-opening of the epoxide substrate. The color change is then exploited for the determination of epoxide using a smartphone as an image acquisition and data processing device, eliminating the need for computer-based image analysis software. The color parameters were systematically evaluated to determine the optimum quantitative analytical parameter. Under optimal conditions, the proposed enzyme-based detection system showed a linear range of 0.13-2mM with a detection limit of 0.07mM and an assay time of 8Min. In addition, the repeatability expressed as relative standard deviation was found to be below 5% (n=6). Validation with gas chromatographic analyses showed that the proposed enzyme-based epoxide detection could be an alternative way in the quantitative determination of epoxides, and particularly useful in resource-limited settings.

KUMBH MELA: a case study for dense crowd counting and modeling

Dense crowd counting and modeling at different gatherings has ignited a new flame in the visual surveillance research community. There is a high possibility of mishappenings in the form of stampede, mob fighting at these gatherings and the administration is helpless in these scenarios. There is a requirement of analyzing the crowd to prevent these dangerous situations. The proposed work is a case study of Kumbh Mela which models the crowd counting in densely populated images. In the proposed work, the orthographic projection of the crowd is captured using a camera attached to a drone, to reduce the effect of occlusion and scaling which, otherwise, may get introduce during image acquisition process. The captured data is fed to a Convolutional Neural Network for training the model to count head of persons present in the frame. The results obtained from the trained model are validated using geometry and imaging techniques. The proposed model has achieved a mean-absolute-error of 94.3 and a mean-squared-error of 104.6 which seems to outperform the existing state-of-the-art models with respect to the reported performance parameters. The proposed model can be used as a viable solution in applications related to modeling the crowd behavior.