Introduction: Biological systems which store medical images should have high reliability in real time. If the bit in an image flips or altered due to voltage ripples or wakes, the information content will be lost, and...
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Introduction: Biological systems which store medical images should have high reliability in real time. If the bit in an image flips or altered due to voltage ripples or wakes, the information content will be lost, and hence the medical diagnosis would be difficult for the clinicians. These bit errors may look like speckles in an image, and thus there is more demand for the despeckling algorithms. Methodology: In common, the median filter plays a crucial role to reduce the speckle noise or impulse noise. This study explores the implementation of the existing method and five novel proposed algorithms to mitigate bit errors in medical images. Here, the proposed techniques are mainly based on point processing, edge detection, and morphological processingalgorithms. The performance metrics like peak signal-to-noise ratio (PSNR), signalto- noise ratio (SNR), quality index (QI), degree of distortion (D), root-mean-square error (RMSE), structural similarity index (SSIM) and the execution time are used for the comparison of the existing method and the proposed algorithms. Results & Conclusion: The existing method and the proposed algorithms are implemented using Matlab R2016b. Simulation results show that the proposed algorithms provide better results than the current approach. For example, the fourth proposed algorithm provides better PSNR (47.28 dB) than others for the abdomen magnetic resonance imaging (MRI) scan image.
This paper presents the implementation of Particle filter based Object tracking using ReconOS on Reconfigurable Computing System. Multithreading can be used to improve the performance of complex imageprocessing algor...
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This paper presents the implementation of Particle filter based Object tracking using ReconOS on Reconfigurable Computing System. Multithreading can be used to improve the performance of complex imageprocessingalgorithms, but their sequential execution is a barrier which can be tackled by the effective use of FPGAs. In order to accomplish the desired performance, an operating system, ReconOS is used on an ARM based CPU-FPGA hybrid platform. ReconOS extends communication and synchronization primitives of operating systems like mutexes, semaphores, condition variable and message boxes to reconfigurable hardware. ReconOS provides the advantage of mapping the particle filter algorithm into reconfigurable hardware and accessing the data from software threads. Thus providing improved performance, portability and unified appearance as well as transparency to the object tracking application.
We propose a GPU-based approach to accelerate filtered backprojection (FBP)-type computed tomography (CT) algorithms by adaptively reconstructing only relevant regions of the object at full resolution. In industrial a...
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We propose a GPU-based approach to accelerate filtered backprojection (FBP)-type computed tomography (CT) algorithms by adaptively reconstructing only relevant regions of the object at full resolution. In industrial applications, the object's insensitivity to radiation as well as lack of inner motion allow for high-resolution scans. The large amounts of recorded data, however, pose serious challenges as the computational cost of CT reconstruction scales quartically with resolution. To ensure real-time reconstruction (i.e. faster processing than projection acquisition) for high-resolution scans, our method skips below-threshold voxels and monotonous regions inside the object. Our approach is able to speed up the reconstruction process by a factor of up to 13 while simultaneously reducing memory requirements by a factor of up to 71.
Increasing the effectiveness of training and training sessions is possible through the implementation of so-called biological feedback. Such feedback allows the teacher, or the instructor, to continuously monitor the ...
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Increasing the effectiveness of training and training sessions is possible through the implementation of so-called biological feedback. Such feedback allows the teacher, or the instructor, to continuously monitor the current psycho-emotional and functional state of the students. As a result, it becomes possible to adapt the style, pace, training mode and the volume of the material outlined, depending on the current receptivity and fatigue level of the listeners. The main element of systems that implement biological feedback in practice are remote non-contact technologies. Such technologies allow in a fully automatic mode to register the main most informative human bio-parameters. Among them, in the first place are the parameters characterizing the current state of the cardiovascular system of man, his breathing system, as well as his peripheral nervous system. The bulk of information is obtained by processing in real time the thermal infrared image of a person’s face. Unfortunately, existing algorithms for distinguishing a person’s face have a sufficiently high computational complexity and insufficient reliability. A typical example in this regard can be a family of algorithms based on the viola-Jones approach. The approach proposed in the work is based on taking into account additional information about the most likely location of a person’s face on a thermal image. This approach is most appropriate to use in cases of quasi-stationary location of people in the room. A typical example is the location of students at the tables in the classroom. For such cases it is possible to determine the areas of the most probable location of the trainees’ faces, as well as the possible boundaries of their movement. Laboratory tests of the developed program on the basis of the proposed algorithm have confirmed its high productivity, as well as efficiency in identifying students faces in the classroom.
In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for l...
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In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for low and high frequency signals. A prerequisite of fusion is the geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel might prove inaccurate on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment allowing us to further fuse the images. It is experimentally shown to produce a high quality of multi-spectral image registration and fusion under challenging scenarios.
A key challenge when displaying and processing sensed real-time 3D data is efficiency of generating and post-processingalgorithms in order to acquire high quality 3D content. In contrast, our approach focuses on volu...
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A key challenge when displaying and processing sensed real-time 3D data is efficiency of generating and post-processingalgorithms in order to acquire high quality 3D content. In contrast, our approach focuses on volumetric generation and processingvolumetric data using an efficient low-cost hardware setting. Acquisition of volumetric data is performed by connecting several Kinect v2 scanners to a single PC that are subsequently calibrated using planar pattern. This process is by no means trivial and requires well designed algorithms for fast processing and quick rendering of volumetric data. This can be achieved by fusing efficient filtering methods such as Weighted median filter (WM), Radius outlier removal (ROR) and Laplace-based smoothing algorithm. In this context, we demonstrate the robustness and efficiency of our technique by sensing several scenes.
The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image a...
The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image analysis and pattern recognition is enhanced by image preprocessing. However, the existing binarization algorithms are not universal for images with different distortions and loss of information. The accuracy of binarization algorithms depends on many factors, such as shadows, uneven lighting, low contrast, noise, etc. images with different characteristics of light and noise are simulated in order to model various lighting conditions on information from digital cameras of robotic systems. The paper investigates global and adaptive image binarization algorithms. The binarized images were obtained using these algorithms and the results of binarized images recognition are compared by an optical character recognition system. An analysis of the comparison results showed that for images made in poor lighting conditions or images with low contrast, or images with high noise levels, adaptive binarization algorithms are better suited. However, in most cases it is not possible to obtain fully correctly recognized *** paper proposes a new binarization method based on artificial neural networks. The process of creating an artificial neural network is shown, include the parameters for determining the class of a pixel, the adjustment of weights, the architecture of an artificial neural network. A comparison of the proposed artificial neural network with existing image binarization algorithms demonstrate that in most cases the artificial neural network has the result of imageprocessing at the level of adaptive algorithms or higher. The proposed method of images binarization based on the image color characteristics analysis allows to solve image recognition tasks by robotized systems.
作者：
Anna v. DoronichevaSergey Z. SavinCC FEB RAS
Federal State Budgetary Institution of Science computing Center of the Far Eastern Branch of the Russian Academy of Sciences Khabarovsk Russia
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the develo...
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ISBN:
(纸本)9781538675328
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the development of computer and telecommunications systems and networks determines the look of the present and future of medical technology and of medicine in general [8], [10]. At last years of one of the directions of development of cloud, computing technologies in high-tech-medicine is a processing the digital image: improvement of quality of image, recovering image, its recognition of separate elements. Recognition of pathological processes is one of the most important problems of processing the medical image. By now, a number of standards for medical imaging have been developed. By analogy with CAD/CAM systems (computer aided design and computer aided manufacturing) for technical applications, CAD (computer-aided diagnosis) systems are being developed for medical purposes. Some of them are already successfully operating, but to date these systems are only "assistants" of a diagnostician who takes decisions. CAD algorithms for medical imaging systems typically include image segmentation, the selection of some objects of interest ("masscs"), their analysis, parametric description of the selected objects and their classification.
In this paper, we develop a two-way multi-relay scheme for JPEG 2000 image transmission. We adopt a modified time-division broadcast cooperative protocol, and derive its power allocation and relay selection under a fa...
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In this paper, we develop a two-way multi-relay scheme for JPEG 2000 image transmission. We adopt a modified time-division broadcast cooperative protocol, and derive its power allocation and relay selection under a fairness constraint. The symbol error probability of the optimal system configuration is then derived. After that, a joint source-channel coding (JSCC) problem is formulated to find the optimal number of JPEG 2000 quality layers for the image and the number of channel coding packets for each JPEG 2000 codeblock that can minimize the reconstructed image distortion for the two users, subject to a rate constraint. Two fast algorithms based on dynamic programming and branch and bound are then developed. Simulation demonstrates that the proposed JSCC scheme achieves better performance and lower complexity than other similar transmission systems.
This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by usin...
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This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by using the machine vision technology. The HALCON imageprocessing library is used as the development platform to build the tool parameters test system. Several algorithms including image segmentation, edge extraction and fitting ellipse determination is used for imageprocessing. The tool parameters such as external diameter and contour angle of tool edge can be obtained after rebuilding the contour of tool edge. The proposed scheme is shown to be reliable and effective for the automated tool parameter measurement.
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