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  • 期刊名称:

    Journal of signal processing systems for signal, image, and video technology

  • 中文名称: 信号,图像和视频技术的信号处理系统杂志
  • 刊频: 0.732
  • ISSN: 1939-8018
  • 出版社: -
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  • 机译 针对CT放射学中技术参数的功能稳健性评估:通过患者数据集验证幻影研究
    摘要: Recent advances in radiomics have shown promising results in prognostic and diagnostic studies with high dimensional imaging feature analysis. However, radiomic features are known to be affected by technical parameters and feature extraction methodology. We evaluate the robustness of CT radiomic features against the technical parameters involved in CT acquisition and feature extraction procedures using a standardized phantom and verify the feature robustness by using patient cases. ACR phantom was scanned with two tube currents, two reconstruction kernels, and two fields of view size. A total of 47 radiomic features of textures and first-order statistics were extracted on the homogeneous region from all scans. Intrinsic variability was measured to identify unstable features vulnerable to inherent CT noise and texture. Susceptibility index was defined to represent the susceptibility to the variation of a given technical parameter. Eighteen radiomic features were shown to be intrinsically unstable on reference condition. The features were more susceptible to the reconstruction kernel variation than to other sources of variation. The feature robustness evaluated on the phantom CT correlated with those evaluated on clinical CT scans. We revealed a number of scan parameters could significantly affect the radiomic features. These characteristics should be considered in a radiomic study when different scan parameters are used in a clinical dataset.
  • 机译 基于BP神经网络和Ada Boost系统混合的脑图像分割。
    • 作者:Chao, Zhen; Kim, Hee-Joung;
    • 刊名:Journal of signal processing systems for signal, image, and video technology
    • 2020年第3期
    摘要: The segmentation of brain magnetic resonance (MR) images can provide more detailed anatomical information, which can be of great help for the proper diagnosis of brain diseases. Therefore, the study of medical image segmentation technology is crucial and necessary. Owing to the presence of equipment noise and the complexity of the brain structure, the existing methods have various shortcomings and their performances are not ideal. In this study, we propose a new method based on back propagation (BP) neural networks and the AdaBoost algorithm. The BP neural network that we created has a 1-7-1 structure. We trained the system using a gravitational search algorithm. (In this algorithm, we use segmented images, which were obtained by state-of-the-art methods, as ideal output data.) Based on this, we established and trained 10 groups of back propagation neural networks (BPNNs) by applying 10 groups of different data. Subsequently, we adopted the AdaBoost algorithm to obtain the weight of each BPNN. Finally, we updated the BPNNs by training the gravitational search and AdaBoost algorithms. In this experiment, we used one group of brain magnetic resonance imaging (MRI) datasets. A comparison with four state-of-the-art segmentation methods through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results for brain MR image segmentation.
  • 机译 使用卷积神经网络从视频荧光图自动分割颈椎间盘及其性能评估
    摘要: Dysphagia has become an important issue in many countries, and there is a strong need for elucidating the causes of dysphagia. One of the promising ways is to analyze the static and dynamic mechanisms of cervical structures, such as epiglottises, hyoid bones, and cervical vertebral bodies, based on medical images. In this study, we propose an automated segmentation method of cervical intervertebral disks (IDs) from videofluorography (VF) by use of a convolutional neural network (CNN). First, cervical masks are extracted from the frame images of VF, and then a patch-based CNN is applied to the cervical masks to obtain the probability images of ID regions. The sizes of patches are changed in a certain range, and pixel values in the patches are normalized. Morphological image filters are applied to the probability images to eliminate false-positive pixels. The proposed method is applied to VF of 58 participants, consisting of 39 healthy people and 19 patients. The segmentation results are compared with the ground truth as determined by a medical doctor and are evaluated with the pixel-wise F-measure. The F-measure is highest (0.880) when the patch size is 21 x 21 pixels and the both of the pixel value normalization (PVN) and the false positive elimination (FPE) are applied. On the other hand, the F-measure is lowest (0.443) when the patch size is 15 x 15 pixels and neither PVN nor FPE is applied.
  • 机译 研究转移学习对基于ROI的胸部CT图像分类的影响:以弥漫性肺部疾病为例
    摘要: Research on Computer-Aided Diagnosis (CAD) of medical images has been actively conducted to support decisions of radiologists. Since deep learning has shown distinguished abilities in classification, detection, segmentation, etc. in various problems, many studies on CAD have been using deep learning. One of the reasons behind the success of deep learning is the availability of large application-specific annotated datasets. However, it is quite tough work for radiologists to annotate hundreds or thousands of medical images for deep learning, and thus it is difficult to obtain large scale annotated datasets for various organs and diseases. Therefore, many techniques that effectively train deep neural networks have been proposed, and one of the techniques is transfer learning. This paper focuses on transfer learning and especially conducts a case study on ROI-based opacity classification of diffuse lung diseases in chest CT images. The aim of this paper is to clarify what characteristics of the datasets for pre-training and what kinds of structures of deep neural networks for fine-tuning contribute to enhance the effectiveness of transfer learning. In addition, the numbers of training data are set at various values and the effectiveness of transfer learning is evaluated. In the experiments, nine conditions of transfer learning and a method without transfer learning are compared to analyze the appropriate conditions. From the experimental results, it is clarified that the pre-training dataset with more (various) classes and the compact structure for fine-tuning show the best accuracy in this work.
  • 机译 基于Savitzky-Golay过滤器的定量动态对比度增强超声评估肝细胞癌小鼠的治疗反应
    摘要: As new drugs are developed in targeted therapy for advanced hepatocellular carcinoma (HCC), an accurate evaluation procedure for the therapeutic efficacy is needed. Current methods use MRI or CT based response evaluation criteria in solid tumors (RECIST) which is unsatisfactory for overlooking the functional response. We propose a new mice model of HCC for assessment of the early response to targeted therapy with contrast-enhanced ultrasound (CEUS). The major technical innovation is analysis of tumor functional characteristics using Savitzky-Golay filter (S-G filter) based CEUS quantification (SGCQ) software. In this study, mice were divided into three groups, including the control group (n(1) = 18), sorafenib treatment group (n(2) = 18) and lenvatinib treatment group (n(3) = 18). SGCQ software specialized in data smoothing was used to quantify the time, enhanced intensity and blood volume related parameters at five different time points within 14 days of therapy. Promising experimental results were obtained. From the analysis, it could detect response as early as 4th day and perfusion time (PT), mean transit time (MTT), area under the curve of tumor/adjacent parenchyma (qAUC), wash-in slope a3, the average time of perfusion (T0) were early predictors. Then, tumors were excised with histopathology performed, CD31 H-score is in correlation with parameters peak intensity (PI), enhanced intensity (EI) and area under the curve of tumor/adjacent parenchyma (qAUC). Moreover, there was no significant difference in efficacy between sorafenib and lenvatinib in both CEUS parameters and histopathology. Finally, the finding of this study proves SGCQ software to be a valid, sensitive and repeatable method for therapeutic evaluation. Quantitative and comparative studies show that sorafenib and lenvatinib, as two first-line targeted drugs, ensure the therapeutic advantages of HCC.
  • 机译 基于多尺度卷积神经网络的冠状动脉纤维斑块检测
    摘要: One of the major causes of the coronary heart disease is vascular stenosis and thrombosis that is generally caused by development of fibrous plaques. Therefore, detection of a fibrous plaque in coronary arteries for the diagnosis and treatment of coronary heart disease is of clinical significance. Technical challenges are in reading the optical coherence tomography (OCT) images which is tedious and inaccurate. In response, we propose an automated coronary artery fibrous plaque detection method based on deep learning with Convolutional Neural Networks (CNN). We present our novel techniques of identifying a contracting path to capture the context and a symmetric expanding path that enables the precise localization. The algorithm utilizes the features of the contracting path and the expanding path, so that the merged features can present the context and accurate localization, and uses the multi-scale feature maps for detection. Experimental results show that the proposed method achieved a coincidence of 91.04%, accuracy of 94.12%, and recall of 94.12%. Compared with the previously published work the proposed method is advantageous in both accuracy and robustness.
  • 机译 利用卷积神经网络增强归一化金属伪影的减少,在金属伪影污染的CT中髋和大腿肌肉的贝叶斯分割
    摘要: In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a Convolutional Neural Network-based segmentation using two U-net architectures. The first U-net refines the result of NMAR and the Bayesian muscle segmentation is performed by the second U-net. We conducted experiments using simulated images of 20 patients and real images of three patients to evaluate the segmentation accuracy of 19 muscles. In simulation study, the proposed method showed statistically significant improvement (p < 0.05) in the average symmetric surface distance (ASD) metric for 12 muscles out of 19 muscles and the average ASD of all muscles from 1.46 +/- 0.904 mm (mean +/- std. over all patients) to 1.30 +/- 0.775 mm over our previous method. Addition to this, the high correlation ratio between segmentation accuracy and the estimated uncertainty was found. The real image study using the manual trace of gluteus maximus and medius muscles showed ASD of 1.89 +/- 0.553 mm.
  • 机译 使用数字混沌的宽带光跳频
    摘要: A wide-band optical frequency hopping (FH) scheme is proposed and analyzed using hyper digital chaos, where the chaotic sequences are applied to encrypt the original data in physical layer during transmission. The multi-fold data encryption is achieved based on the dynamic generation of the optical carrier frequency, FH rate as well as the available frequency set respectively. A hyper digital chaos is used to generate the multiple, independent chaotic sequences for the proposed data encryption, as a result, a huge key space of 10(58) is provided to enhance the security. The performances of data transmission and security enhancement against various attacks are evaluated in details.
  • 机译 M3U:用于多类分类的最小平均最小不确定性特征选择
    摘要: This paper presents a novel multiclass feature selection algorithm based on weighted conditional entropy, also referred to as uncertainty. The goal of the proposed algorithm is to select a feature subset such that, for each feature sample, there exists a feature that has a low uncertainty score in the selected feature subset. Features are first quantized into different bins. The proposed feature selection method first computes an uncertainty vector from weighted conditional entropy. Lower the uncertainty score for a class, better is the separability of the samples in that class. Next, an iterative feature selection method selects a feature in each iteration by (1) computing the minimum uncertainty score for each feature sample for all possible feature subset candidates, (2) computing the average minimum uncertainty score across all feature samples, and (3) selecting the feature that achieves the minimum of the mean of the minimum uncertainty score. The experimental results show that the proposed algorithm outperforms mRMR and achieves lower misclassification rates using various types of publicly available datasets. In most cases, the number of features necessary for a specified misclassification error is less than that required by traditional methods. For all datasets, the misclassification error is reduced by 5 similar to 25% on average, compared to a traditional method.
  • 机译 蛙叫分类的声学和视觉特征研究
    摘要: Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls.
  • 机译 x86多核处理器上非二进制LDPC码的高吞吐量FFT-SPA解码器实现
    摘要: Low-Density Parity-Check (LDPC) codes are a well known Error Correction Code family used for instance, in wireless and satellite communication links. Error correction performance of LDPC codes was further enhanced by extending it to higher order Galois fields, giving rise hence to the so-called non-binary LDPC codes (NB-LDPC). Error correction performance improvement (CCSDS 2014) is the main reason behind the adoption by the Consultative Committee for Space Data Systems (CCSDS) of the NB-LDPC codes in the experimental specification for the future next generation uplinks (CCSDS 2014, 2015). The high error correction efficiency for short frames make NB-LDPC codes a good candidate for IoT applications. However, the performance gain comes at the expense of a high decoding computational complexity (CCSDS 2014; Conde-Canencia et al. 2009). In this paper, an x86 multicore NB-LDPC decoder implementation is provided. This decoder that implements the FFT-SPA algorithm provides a throughput improvement of about 1.3x to 2.7x, a latency reduction of more than 95% and a power consumption halved in comparison with the most efficient works on GPU (Graphics Processing Unit) device. Indeed, an efficient memory mapping and computation optimizations on the x86 architecture enable to achieve a higher decoding throughput than the GPU-based in similar experimental setup. Consequently, the throughput efficiency, the low processing latency associated with a low power consumption makes this proposed multicore implementation practical and attractive for real time implementations of NB-LDPC decoders in future SDR or Cloud-RAN systems for CCSDS standard and IoT applications.
  • 机译 附加攻击条件下数字图像克隆区域的检测方法
    摘要: The presented new method for detecting of clone areas in a digital image is effective in conditions of disturbing effects on the image that are additional to the cloning, including cases of a small area of clone area (less than 0.4% of the image). The method is based on the detection of geometric comparability of surface parts, which is compared with digital image, corresponding to areas of the clone and its prototype. The quantitative index was found. This index is characterizing the individual areas of the image and coinciding for areas of clone and its prototype, including under attack conditions. This index is the value of the local (global) minimum of function values that interpolate the elements of the matrix G, which is put into correspondence to the analyzed digital image matrix. The elements G represent the smallest difference between the corresponding block of the digital images matrix q x q from any other of its block. The results of the computational experiment are presented. They are confirmed high efficiency of this method in compare to the existing analogues, regardless of the specifics of attack conducted on cloned image, the relative sizes of the cloned area, block sizes used in the image analysis.
  • 机译 基于GPGPU的光谱相关密度函数的并行实现
    摘要: In this study, the parallelization of a critical statistical feature of communication signals called the spectral correlation density (SCD) is investigated. The SCD is used for synchronization in OFDM-based systems such as LTE and Wi-Fi, but is also proposed for use in next-generation wireless systems where accurate signal classification is needed even under poor channel conditions. By leveraging cyclostationary theory and classification results, a method for reducing the computational complexity of estimating the SCD for classification purposes by 75% or more using the Quarter SCD (QSCD) is proposed. We parallelize the SCD and QSCD implementations by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based SCD implementation from 120 signals/second to 3300 signals/second.
  • 机译 POLYBiNN:使用决策树的神经网络二进制推理引擎
    摘要: Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) have gained significant popularity in several classification and regression applications. The massive computation and memory requirements of DNN and CNN architectures pose particular challenges for their FPGA implementation. Moreover, programming FPGAs requires hardware-specific knowledge that many machine-learning researchers do not possess. To make the power and versatility of FPGAs available to a wider deep learning user community and to improve DNN design efficiency, we introduce POLYBiNN, an efficient FPGA-based inference engine for DNNs and CNNs. POLYBiNN is composed of a stack of decision trees, which are binary classifiers in nature, and it utilizes AND-OR gates instead of multipliers and accumulators. POLYBiNN is a memory-free inference engine that drastically cuts hardware costs. We also propose a tool for the automatic generation of a low-level hardware description of the trained POLYBiNN for a given application. We evaluate POLYBiNN and the tool for several datasets that are normally solved using fully connected layers. On the MNIST dataset, when implemented in a ZYNQ-7000 ZC706 FPGA, the system achieves a throughput of up to 100 million image classifications per second with 90 ns latency and 97.26% accuracy. Moreover, POLYBiNN consumes 8x less power than the best previously published implementations, and it does not require any memory access. We also show how POLYBiNN can be used instead of the fully connected layers of a CNN and apply this approach to the CIFAR-10 dataset.
  • 机译 使用CUR算法的BCI转移学习
    摘要: The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject's EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression.
  • 机译 介绍用于自动调制分类的高效统计模型
    摘要: Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as spectrum management, cognitive radio, intelligent modems, and interference identification. In this paper, a new robust AMC algorithm based on Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is proposed. Primarily, multi-level wavelet transform is applied on the received data samples. To select the efficient statistical model for wavelet coefficient description, the statistical characteristics of these coefficients are surveyed. The proposed analysis precisely illustrates that these coefficients have heteroscedasticity property which has not been mentioned before. Subsequently, to describe the wavelet coefficients, the heteroscedastic GARCH model is employed and its parameters are extracted as the features. Finally, the obtained features are applied to support vector machine (SVM) classifier to simultaneously determine the modulation type and constellation size. Eleven different types and sizes of the digital modulation schemes in various channels such as AWGN, flat fading and multipath fading in presence of common channel impairments are examined. The experimental results reveal the superior performance of the proposed method in comparison with the previously introduced approaches.
  • 机译 基于贝叶斯边界-Ness的最优分类器参数状态选择实用方法
    摘要: We propose a novel practical method for finding the optimal classifier parameter status corresponding to the Bayes error (minimum classification error probability) through the evaluation of estimated class boundaries from the perspective of Bayes boundary-ness. While traditional methods approach classifier optimality from the angle of minimization of the estimated classification error probabilities, we approach it from the angle of optimality of the estimated classification boundaries. The optimal classification boundary consists solely of uncertain samples, whose posterior probability is equal for the two classes separated by the boundary. We refer to this essential characteristic of the boundary as "Bayes boundary-ness", and use it to measure how optimal the estimated boundary is. Our proposed method achieves the optimal parameter status using the training data only once, in contrast to such traditional methods as Cross-Validation (CV), which demand separate validation data and often require a number of repetitions of training and validation. Moreover, it can be directly applied to any type of classifier, and potentially to any type of sample. In this paper, we first elaborate on our proposed method that implements the Bayes boundary-ness with an entropy-based uncertainty measure. Next, we analyze the mathematical characteristics of the uncertainty measure adopted. Finally, we evaluate the method through a systematic experimental comparison with CV-based Bayes boundary estimation, which is known to be highly reliable in the Bayes error estimation. From the analysis, we rigorously show the theoretical validity of our adopted uncertainty measure. Moreover, from the experiment, we successfully demonstrate that our method can closely approximate the CV-based Bayes boundary estimate and its corresponding classifier parameter status with only a single-shot training over the data in hand.
  • 机译 在FPGA中有效利用DSP的新HLS分配算法。
    摘要: In this paper, an algorithm of allocation for FPGA dedicated HLS flow is proposed. This algorithm takes as input a Data Flow Graph (DFG) and provides an optimized implementation of the considered DFG. The proposed approach allows an efficient resource utilization thanks to series of tests and processes done on the DFG's nodes. We compare our method considering different design goals against several implementation techniques. The results show an enhancement in terms of LUT utilization reaching up to 89%. The power consumption has also been improved by up to 37%. Finally the maximum frequency increased significantly thanks to the efficient use of FPGA DSP blocks.
  • 机译 H.264 / AVC编码的基于最可能模式和SATD的帧内模式决策的早期终止
    • 作者:Wang, Ping; Cheng, Hao;
    • 刊名:Journal of signal processing systems for signal, image, and video technology
    • 2020年第2期
    摘要: Intra coding in H.264/AVC can significantly improve the coding efficiency but at the cost of high computational complexity due to the use of rich prediction modes and rate-distortion optimization technique. In this paper, an early termination algorithm of intra mode decision is presented to address the complexity issue in 4x4 intra prediction. The proposed algorithm is motivated by two facts: the most probable mode defined based on the spatial similarity has a high possibility to be the best mode, and a good prediction usually has a small residual block which is measured by the sum of absolute transformed difference (SATD). The most probable mode and two modes with the smallest SATD values are investigated to select the candidate modes by using the proposed early termination rules. Experimental results show that the proposed algorithm effectively reduces the complexity of 4x4 intra prediction while maintaining almost the same coding performance on peak signal-to-noise ratio and bit rate compared with the full search algorithm.
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