Image Processing Platform Research Articles

Cable surface damage detection in cable-stayed bridges using optical techniques and image mosaicking

A vision inspection system is developed for detecting surface damages on cables in long-span cable-stayed bridges. The system consists of a climbing robot, an image processing platform, and 4 fixed cameras. While the climbing robot loads the whole system and ascends along the cables, 4 cameras can continuously acquire cable surface images. Then, image processing techniques can be employed to achieve defect identification. In our optical system, a single image may only collect a part of the whole defect because of the field of view (FOV) of each camera. In order to obtain the morphology of the whole defect, we present an efficient scale-invariant feature transform (SIFT) algorithm to achieve the multi-image mosaic with partially overlapped regions in different defect images. Experimental results demonstrate that the proposed vision inspection technologies are suitable for application to the bridge cables for defect detection and condition assessment.

The utility of Random Forests for wildfire severity mapping

Reliable fire severity mapping is a vital resource for fire scientists and land management agencies globally. Satellite derived pre- and post-fire differenced severity indices (∆FSI), such as the differenced Normalised Burn Ratio (∆NBR), are widely used to map the severity of large wildfires. Fire severity classification is commonly undertaken through the identification of severity class thresholds in ∆FSI. However, several shortcomings have been identified with severity classifications using ∆FSI, including poor delineation of low fire severity classes, and unsatisfactory performance when ∆FSI classification thresholds are applied to new landscapes. Our study assesses the performance of the Random Forest classifier (RF) for improving the accuracy of satellite based wildfire severity mapping across heterogeneous landscapes using Landsat imagery. We collected point based fire severity training data (n = 10,855) from sixteen large wildfires occurring across south-eastern Australia between 2006 and 2016. The predictive accuracy of fire severity classification using ∆NBR and the RF incorporating numerous spectral indices, was assessed using bootstrapping and cross validation. Image acquisition and index calculation for each fire was undertaken in Google Earth Engine (GEE). Results of the bootstrapping validation show that the RF classifier had very high classification accuracy (>95%) for unburnt (UB), crown scorch (CS) and crown consumption (CC) severity classes, and high classification accuracy (>74%) for low severity classes (crown unburnt, CU; partial crown scorch, PCS). The RF classification outperformed the ∆NBR classification for all severity classes, increasing classification accuracy by between 6%–21%. Cross validation using independent fires produced similar median classification accuracy as the bootstrapping validation, though the RF classification of CU was substantially reduced to ~55%. ∆NBR, ∆NDWI and ∆NDVI were the three most important variables in the RF model. The Landsat satellite platform used to derive the indices had little effect on classification accuracy. Maps produced using the RF classifier in GEE had similar spatial patterns in fire severity classes as maps produced using time-consuming hand digitisation of aerial images. GEE was found to be a highly efficient platform for image acquisition, processing and production of severity maps. Our study shows that fire severity mapping using RF classifiers provides a robust method for broad scale mapping of fire severity across heterogeneous landscapes. Furthermore, the GEE-based classification framework supports the operational application of this approach in a land management agency context for the rapid production of fire severity maps.

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.

KREAP: an automated Galaxy platform to quantify in vitro re-epithelialization kinetics.

Background In vitro scratch assays have been widely used to study the influence of bioactive substances on the processes of cell migration and proliferation that are involved in re-epithelialization. The development of high-throughput microscopy and image analysis has enabled scratch assays to become compatible with high-throughput research. However, effective processing and in-depth analysis of such high-throughput image datasets are far from trivial and require integration of multiple image processing and data extraction software tools.FindingsWe developed and implemented a kinetic re-epithelialization analysis pipeline (KREAP) in Galaxy. The KREAP toolbox incorporates freely available image analysis tools and automatically performs image segmentation and feature extraction of each image series, followed by automatic quantification of cells inside and outside the scratched area over time. The enumeration of infiltrating cells over time is modeled to extract three biologically relevant parameters that describe re-epithelialization kinetics. The output of the tools is organized, displayed, and saved in the Galaxy environment for future reference.ConclusionsThe KREAP toolbox in Galaxy provides an open-source, easy-to-use, web-based platform for reproducible image processing and data analysis of high-throughput scratch assays. The KREAP toolbox could assist a broad scientific community in the discovery of compounds that are able to modulate re-epithelialization kinetics.

RIDE: real-time massive image processing platform on distributed environment

As the demand for real-time data processing increases, a high-speed processing platform for large-scale stream data becomes necessary. For fast processing large-scale stream data, it is essential to use multiple distributed nodes. So far, there have been few studies on real-time massive image processing through efficient management and allocation of heterogeneous resources for various user-specified nodes on distributed environments. In this paper, we shall present a new platform called RIDE (Real-time massive Image processing platform on Distributed Environment) which efficiently allocates resources and executes load balancing according to the amount of stream data on distributed environments. It minimizes communication overhead by using a parallel processing strategy which handles the stream data considering both coarse-grained and fine-grained parallelism simultaneously. Coarse-grained parallelism is achieved by the automatic allocation of input streams onto partitions of broker buffer each processed by its corresponding worker node, and maximized by adaptive resource management which adjusts the number of worker nodes in a group according to the frame rate in real time. Fine-grained parallelism is achieved by parallel processing of task on each worker node and maximized by allocating heterogeneous resources such as GPU and embedded machines appropriately. Moreover, it provides a scheme of application topology which has a great advantage for higher performance by configuring the worker nodes of each stage using adaptive heterogeneous resource management. Finally, it supports dynamic fault tolerance for real-time image processing through the coordination between components in our system.

Application of Inverse Perspective Mapping for Advanced Driver Assistance Systems in Automotive Embedded Systems

<span style="font-size: 9.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;">In the recent times vehicle manufactures and automotive suppliers are progressing towards building vision based subsystems for provisioning driver assistance while targeting the automotive safety critical needs. While the acquired images constitute the fundamental input for any vision based system, transforms on images become essential to derive and gain insight into certain specific features. These derived features are used and reused at multiple places for varied automotive applications. This situation warrants a scalable and flexible image processing platform for a class of automotive applications. An attempt is made in this Research work to propose architecture that, specially, includes a layer of image transformations and to implement a prototype image processing platform. Inverse Perspective Mapping (IPM), a widely used class of transforms is emphasized in the present architecture alongside other nominal transforms. Lane departure warning system is implemented on this platform for the purpose of illustration and to analyze the effectiveness of the proposed architecture</span>

Quantitative imaging mass spectroscopy reveals roles of heme oxygenase-2 for protecting against transhemispheric diaschisis in the brain ischemia.

Carbon monoxide-generating heme oxygenase-2 is expressed in neurons and plays a crucial role for regulating hypoxic vasodilation through mechanisms unlocking carbon monoxide-dependent inhibition of H2S-generating cystathionine β-synthase expressed in astrocytes. This study aims to examine whether heme oxygenase-2 plays a protective role in mice against stroke. Focal ischemia was induced by middle cerebral artery occlusion. Regional differences in metabolites among ipsilateral and contralateral hemispheres were analysed by quantitative imaging mass spectrometry equipped with an image-processing platform to optimize comparison of local metabolite contents among different animals. Under normoxia, blood flow velocity in precapillary arterioles were significantly elevated in heme oxygenase-2-null mice vs controls, while metabolic intermediates of central carbon metabolism and glutamate synthesis were elevated in the brain of heme oxygenase-2-null mice, suggesting greater metabolic demands to induce hyperemia in these mice. In response to focal ischemia, heme oxygenase-2-null mice exhibited greater regions of ischemic core that coincide with notable decreases in energy metabolism in the contralateral hemisphere as well as in penumbra. In conclusion, these findings suggest that heme oxygenase-2 is involved in mechanisms by which not only protects against compromised energy metabolism of the ipsilateral hemisphere but also ameliorates transhemispheric diaschisis of the contralateral hemisphere in ischemic brain.

Research and implementation of target tracking algorithm in compression domain on miniaturized DSP platform

The target tracking algorithm in compression domain based on compression perception is studied. To meet the specific application requirements, the shortcomings of original algorithm are improved. At the same time, based on the design idea and demand of miniaturized target position detector, a real-time image processing platform with TMS320DM6437 digital signal processor as the core is designed and implemented, and the algorithm is im-plemented and optimized on the DSP platform. The simulation and experiment results show that after the combination of Kalman filter, LBP feature and adding adaptive learning rate update strategy, the stability of the algorithm is improved. For the implementation in DSP, after a series of optimizing measures, as for an image with resolution of 960×960, taking the target window of 80×80 into account, the computation speed can be up to 25 fps, which can meet the requirement of real-time tracking. The embedded tracking system can track the selected moving objects continuously and stably, and can meet the target localization and tracking requirements under specific applications, which has a real practical value. Moreover, the method in this paper has a certain reference value for the research and applications of this kind of target tracking method in the embedded platform.

On-Board Ortho-Rectification for Images Based on an FPGA

The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique based on a field programmable gate array (FPGA) platform that can be implemented on board spacecraft for (near) real-time processing. The proposed FPGA-based ortho-rectification method contains three modules, i.e., a memory module, a coordinate transformation module (including the transformation from geodetic coordinates to photo coordinates, and the transformation from photo coordinates to scanning coordinates), and an interpolation module. Two datasets, aerial images located in central Denver, Colorado, USA, and an aerial image from the example dataset of ERDAS IMAGINE 9.2, are used to validate the processing speed and accuracy. Compared to traditional ortho-rectification technology, the throughput from the proposed FPGA-based platform and the personal computer (PC)-based platform are 11,182.3 kilopixels per second and 2582.9 kilopixels per second, respectively. This means that the proposed FPGA-based platform is 4.3 times faster than the PC-based platform for processing the same RS images. In addition, the root-mean-square errors of the planimetric coordinates φX and φY and the distance φS are 1.09 m, 1.61 m, and 1.93 m, respectively, which can meet the requirements of correction accuracy in practice.

The vision guidance and image processing of AGV

Firstly, the principle of AGV vision guidance is introduced and the deviation and deflection angle are measured by image coordinate system. The visual guidance image processing platform is introduced. In view of the fact that the AGV guidance image contains more noise, the image has already been smoothed by a statistical sorting. By using AGV sampling way to obtain image guidance, because the image has the best and different threshold segmentation points. In view of this situation, the method of two-dimensional maximum entropy image segmentation is used to solve the problem. We extract the foreground image in the target band by calculating the contour area method and obtain the centre line with the least square fitting algorithm. With the help of image and physical coordinates, we can obtain the guidance information.

Use of imaging software for assessment of the associations among zona pellucida thickness variation, assisted hatching, and implantation of day 3 embryos.

The aim of this study was to determine if zona pellucida thickness variation (ZPTV) is associated with implantation and if this relationship changes with use of assisted hatching (AH). Day 3 embryos from single or double embryo transfers (DETs) performed between 2014 and 2016 were included. ZPTV was assessed by examining photographs taken before transfer using an automated image processing platform to segment the zona pellucida (ZP) with an active contour technique. One hundred points were obtained of ZP thickness (ZPT) of each embryo to calculate ZPTV ([maximum ZPT-mean ZPT]/mean ZPT). Logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) of implantation by tertile of ZPTV. Maternal age and AH were adjusted for a priori. Other cycle and embryo characteristics were adjusted for if they altered the continuous effect estimate by >10%. There was no statistically significant association between ZPTV and implantation across tertiles although embryos with greater ZPTV showed a trend of decreased implantation (Tertile 2 (T2) versus Tertile 1 (T1), OR=0.80, CI=0.50-1.28; Tertile 3 (T3) versus Tertile 1 (T3), OR=0.75, CI=0.47-1.20). While similar nonsignificant trends for the association between ZPTV and implantation were observed across tertiles after stratification of embryos hatched or not, embryos with the greatest ZPTV had slightly higher odds for implantation when AH was utilized (T3 vs. T1: with AH, OR = 0.89, CI = 0.49-1.62; without AH, OR = 0.61, 0.29-1.27). ZPTV was not associated with implantation after day 3 transfer. This finding did not vary by use of AH.

Development of a Mechanical Scanning Device With High-Frequency Ultrasound Transducer for Ultrasonic Capsule Endoscopy.

Wireless capsule endoscopy has opened a new era by enabling remote diagnostic assessment of the gastrointestinal tract in a painless procedure. Video capsule endoscopy is currently commercially available worldwide. However, it is limited to visualization of superficial tissue. Ultrasound (US) imaging is a complementary solution as it is capable of acquiring transmural information from the tissue wall. This paper presents a mechanical scanning device incorporating a high-frequency transducer specifically as a proof of concept for US capsule endoscopy (USCE), providing information that may usefully assist future research. A rotary solenoid-coil-based motor was employed to rotate the US transducer with sectional electronic control. A set of gears was used to convert the sectional rotation to circular rotation. A single-element focused US transducer with 39-MHz center frequency was used for high-resolution US imaging, connected to an imaging platform for pulse generation and image processing. Key parameters of US imaging for USCE applications were evaluated. Wire phantom imaging and tissue phantom imaging have been conducted to evaluate the performance of the proposed method. A porcine small intestine specimen was also used for imaging evaluation in vitro. Test results demonstrate that the proposed device and rotation mechanism are able to offer good image resolution ( [Formula: see text]) of the lumen wall, and they, therefore, offer a viable basis for the fabrication of a USCE device.

Radiation Dose Reduction during Uterine Fibroid Embolization Using an Optimized Imaging Platform

PurposeTo assess radiation dose reduction during uterine fibroid embolization (UFE) using an optimized angiographic processing and acquisition platform. Materials and MethodsRadiation dose data for 70 women (mean age, 46 y; range, 34–67 y) who underwent UFE were retrospectively analyzed. Twenty-one patients underwent UFE using the baseline fluoroscopic and angiographic image acquisition platform, and 49 underwent UFE after implementing an optimized imaging platform in otherwise identical angiography suites. Cumulative kerma-area product (CKAP), cumulative air kerma (CAK), total fluoroscopy time, and image exposure number were collected for each procedure. Image quality was assessed by 3 interventional radiologists blinded to the platform used for image acquisition and processing. ResultsPatients undergoing UFE using the new x-ray fluoroscopy platform had significantly lower CKAP and CAK indicators than patients for whom baseline settings were used. Mean CKAP decreased by 60% from 438.5 Gy · cm2 (range, 180.3–1,081.1 Gy · cm2) to 175.2 Gy · cm2 (range, 47.1–757.0 Gy · cm2; P < .0001). Mean CAK decreased by 45% from 2,034.2 mGy (range, 699.3–5,056.0 mGy) to 1,109.8 mGy (range, 256.6–4,513.6 mGy; P = .001). No degradation of image quality was identified through qualitative evaluation. ConclusionsSignificant reduction in patient radiation dose indicators can be achieved with use of an optimized image acquisition and processing platform.

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

Design and evaluation of medical ultrasonic adaptive beamforming algorithm implementation on heterogeneous embedded computing platform

Medical ultrasonic imaging has been utilized in a variety of clinical diagnoses for many years. Recently, because of the needs of portable and mobile medical ultrasonic diagnoses, the development of real-time medical ultrasonic imaging algorithms on embedded computing platforms is a rising research direction. Typically, delay-and-sum beamforming algorithm is implemented on embedded medical ultrasonic scanners. Such algorithm is the easiest to implement at real-time frame rate, but the image quality of this algorithm is not high enough for complicated diagnostic cases. As a result, minimum-variance adaptive beamforming algorithm for medical ultrasonic imaging is considered in this paper, which shows much higher image quality than that of delay-and-sum beamforming algorithm. However, minimum-variance adaptive beamforming algorithm is a complicated algorithm with O(n3) computational complexity. Consequently, it is not easy to implement such algorithm on embedded computing platform at real-time frame rate. On the other hand, GPU is a well-known parallel computing platform for image processing. Therefore, embedded GPU computing platform is considered as a potential real-time implementation platform of minimum-variance beamforming algorithm in this paper. By applying the described effective implementation strategies, the GPU implementation of minimum-variance beamforming algorithm performed more than 100 times faster than the ARM implementation on the same heterogeneous embedded platform. Furthermore, platform power consumptions, computation energy efficiency, and platform cost efficiency of the experimental heterogeneous embedded platforms were also evaluated, which demonstrated that the investigated heterogeneous embedded computing platforms were suitable for real-time portable or mobile high-quality medical ultrasonic imaging device constructions.

SCIENTIFIC APPROACH TO SELECTION OF A HIGH-PERFORMANCE HETEROGENEOUS COMPUTING PLATFORM FOR IMAGE PROCESSING

The article substantiates the development of a scientific approach to the design of a high-performance heterogeneous computing platform (VGVP) for image processing. As an applied problem for realization on VGVP, the task of image processing in the regime of hard real time, coming through communication channels from diverse specialized information systems, was chosen. The considered scientific approaches realize the concept of creating a multi-module architecture, in which simultaneously several computational modules with a heterogeneous architecture can be processed by information flows. The article shows that heterogeneity, architectural solutions and program mechanisms of interaction of various technologies and technologies. VGVP for heterogeneous parallel-pipeline data processing.

RayPlus: a Web-Based Platform for Medical Image Processing.

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

Research and Application of Collaborative Processing Technology for Graphics and Image Based on HTML5

Based on the above defects and the superiority of HTMLS, the paper is aimed at realizing the process of graphics and image on Web by combining with graphics and image processing algorithm on computer at present. At the same time, the real-time graphics and image processing cooperatively by multiple personnel is also realized. Finally, a graphics and image processing platform on Web will be established which is easy to maintain, share, expand, being carried out in real time and iteratively. It is named as Web Photoshop. The platform is not only able to carry out graphics and image processing work on a single computer, but also used for graphics and image processing cooperatively by multiple personnel. At the same time, the subject also analyzes and discusses the possibility of graphics and image processing cooperatively by multiple personnel by comparing the effect of common graphics and image processing program on desktop. What is more, the HTMLS is used in trial in the subject to rapidly construct the graphics and image processing application based on mobile devices.

Objective characterization of airway dimensions using image processing

ObjectivesWith the evolution of medical and surgical management for pediatric airway disorders, the development of easily translated techniques of measuring airway dimensions can improve the quantification of outcomes of these interventions. We have developed a technique that improves the ability to characterize endoscopic airway dimensions using common bronchoscopic equipment and an open-source image-processing platform. MethodsWe validated our technique of Endoscopic Airway Measurement (EAM) using optical instruments in simulation tracheas. We then evaluated EAM in a large animal model (Ovis aries, n = 5), comparing tracheal dimensions obtained with EAM to measurements obtained via 3-D fluoroscopic reconstruction. The animal then underwent resection of the measured segment, and direct measurement of this segment was performed and compared to radiographic measurements and those obtained using EAM. ResultsThe simulation tracheas had a direct measurement of 13.6, 18.5, and 24.2 mm in diameter. The mean difference of diameter in simulation tracheas between direct measurements and measurements obtained using EAM was 0.70 ± 0.57 mm. The excised ovine tracheas had an average diameter of 18.54 ± 0.68 mm. The percent difference in diameter obtained from EAM and from 3-D fluoroscopic reconstruction when compared to measurement of the excised tracheal segment was 4.98 ± 2.43% and 10.74 ± 4.07% respectively. Comparison of these three measurements (EAM, measurement of resected trachea, 3-D fluoroscopic reconstruction) with repeated measures ANOVA demonstrated no statistical significance. ConclusionsEndoscopic airway measurement (EAM) provides equivalent measurements of the airway with the improved versatility of measuring non-circular and multi-level dimensions. Using optical bronchoscopic instruments and open-source image-processing software, our data supports preclinical and clinical translation of an accessible technique to provide objective quantification of airway diameter.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons

BackgroundAutomated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. New methodIn this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. ResultsExtensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. Comparison with existing methodsWe include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. ConclusionsOur algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.