检索结果-内蒙古大学图书馆

Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

来源：评论

学校读者我要写书评

暂无评论

Seismic Event Detection via Deep Multi-Task Learning

Seismic Event Detection via Deep Multi-Task Learning

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yang Yu Lei Zhang Jiakai Shen Qingcai Wang Guiquan Liu School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

It is fundamental to detect seismic events reliably and efficiently when processing continuous waveform data recorded by seismic stations. Recently, convolutional neural network (CNN) based detecting methods have been proposed for seismic events detection and obtained great success in this area, where the learning of seismic event detecting network of all seismic stations is considered as one learning task and numerous labeled data need to be collected for training the detecting network. However, they tend to ignore the differences between seismic stations caused by geographic position. Moreover, due to a few seismic activities and high cost of manual data labeling, in some areas, the labeled data for seismic event detecting tasks is insufficient. Under this condition, these methods always encounter over-fitting problem leading to bad detection performance. In this paper, we propose a multi-task based framework based on convolutional neural network for accurate seismic event detection under the condition of insufficient labeled data. Specifically, we first cluster the seismic stations into several station clusters and treat the learning of seismic event detecting network of every station cluster as a learning task, and then we propose a deep multi-task network named detectMTIA among multiple tasks. Experimental results on a real-world seismic dataset with nine stations demonstrate the effectiveness of the proposed framework, especially when the labeled data is insufficient.

关键词： Task analysis Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Convergence Analysis of Whale Optimization Algorithm

引用

Journal of Physics: Conference Series 2021年第1期1757卷

作者： Wentao Feng National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan 610041 China

The whale optimization algorithm (WOA) has been widely used in different applications. It has simple control parameters and novel population updating mechanism. However, there is few theoretical analysis of WOA and the convergence property of WOA is ambiguity. This paper analyzes the convergence property of WOA by using the Markov chain of the stochastic process theory. The Markov chain model of the WOA algorithm is established. The one step transition probabilities and convergence properties of different population updating mechanisms in WOA are given. It's proved that the convergence property of WOA is determined by its shrinking encircling mechanism. Finally, three algorithms with different population updating mechanisms are tested with thirteen benchmark functions on accuracy and convergence speed. The simulation results on benchmark functions verify the validity of the theoretical analysis of WOA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Experiment and Raw Data processing of Heterogeneous Bistatic Forward-looking SAR 6

Experiment and Raw Data Processing of Heterogeneous Bistatic...

引用

6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Song, Xuan Li, Yachao Wu, Chunfeng Wang, Zongfu Mei, Haiwen Key Laboratory for Radar Signal Processing Xidian University Xi'an710071 China China Sanjiang Space Group Wuhan430040 China Chengdu Huirong Guoke Microsystem Technology Co. Ltd Chengdu610000 China

ISBN: (纸本)9781728129129

Bistatic forward-looking SAR is a synthetic aperture radar system with an independently moving transmitter and receiver breaks through the limitations of the conventional monostatic SAR on the forward-looking imaging. However, the existence of a double hyperbola in the bistatic range equation makes the severe two-Dimension (2-D) coupling. Based on the bistatic SAR space geometrical model and the point target spectrum form the series reversion, this paper introduces the result of bistatic forward-looking SAR raw data processed using polynomial NCS algorithm is given to confirmed the applicability of mentioned algorithm. Furthermore, an analysis about the SAR image feature is discussed, then a produce is defined to slove the problem which may reduce the SNR of bistatic SAR is proposed. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A Modified Information Embedding Scheme for the Integrated System of radar and Communication

A Modified Information Embedding Scheme for the Integrated S...

引用

signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Wenhua Wu Yunhe Cao Yutao Liu Science and Technology on Communication Networks Laboratory Shi jia zhuang China National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

This paper has proposed a modified information embedding scheme for the integrated system of radar and communication without influence on the mainlobe. Exploiting the waveform diversity of multiple-input multiple-output (MIMO) radar, the beamforming weight vectors are designed for constellation mapping to increase the data rate. Simulation results are presented to demonstrate the theoretical analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance

引用

Research on Biomedical Engineering 2019年第1期35卷 45-56页

作者： Sais, Barbara Teixeira Vital, Daniel Aparecido Moraes, Matheus Cardoso Laboratory of Image and Signal Processing of the Institute of Science and Technology Federal University of São Paulo – UNIFESP 330 Talim St. room 108 Jardim Aeroporto CEP São José dos CamposSP12231-280 Brazil

Purpose: More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations. Methods: The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise;(2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors;and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated. Results: The material is composed of a set of 90 CT chest volumes. We obtained high accuracy parameters, such as Overlap Dice (%) = 98.77 ± 0.58 and False Negative (%) = 0.13 ± 0.026. Conclusion: The proposed methodology presented significant accuracy values and processing time, having a large database to test the methodology, besides being a fully automatic method of 3D bone reconstruction. © 2019, Sociedade Brasileira de Engenharia Biomedica.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Interactive prototype learning and self-learning for few-shot medical image segmentation

引用

Artificial Intelligence in Medicine 2025年 167卷

作者： Yuhui Song Chenchu Xu Boyan Wang Xiuquan Du Jie Chen Yanping Zhang Shuo Li School of Computer Science and Technology Anhui University 230601 Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University 230601 Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center 230051 Hefei China Department of Computer Science and Technology Tsinghua University 100084 Beijing China School of Engineering Case Western Reserve University 44106 Cleveland United States

Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the class knowledge of a few known (support) samples and extend it to unknown (query) samples. However, the large distribution differences between the support image and the query image lead to serious deviations in the transfer of class knowledge, which can be specifically summarized as two segmentation challenges: Intra-class inconsistency and Inter-class similarity, blurred and confused boundaries. In this paper, we propose a new interactive prototype learning and self-learning network to solve the above challenges. First, we propose a deep encoding-decoding module to learn the high-level features of the support and query images to build peak prototypes with the greatest semantic information and provide semantic guidance for segmentation. Then, we propose an interactive prototype learning module to improve intra-class feature consistency and reduce inter-class feature similarity by conducting mid-level features-based mean prototype interaction and high-level features-based peak prototype interaction. Last, we propose a query features-guided self-learning module to separate foreground and background at the feature level and combine low-level feature maps to complement boundary information. Our model achieves competitive segmentation performance on benchmark datasets and shows substantial improvement in generalization ability.

关键词： Few-shot segmentation Medical image segmentation Prototype learning Self-learning

来源：评论

学校读者我要写书评

暂无评论

Multiple sclerosis segmentation method in magnetic resonance imaging using fuzzy connectedness, binarization, mathematical morphology, and 3D reconstruction

引用

Research on Biomedical Engineering 2020年第3期36卷 291-301页

作者： de Arruda, André Luiz Costa Vital, Daniel Aparecido Kitamura, Felipe Campos Abdala, Nitamar Moraes, Matheus Cardoso Laboratory of Image and Signal Processing of the Institute of Science and Technology of Federal University of São Paulo UNIFESP 330 Talim St. room 108-Jardim Aeroporto CEP São José dos CamposSP12231-280 Brazil Department of Imaging Diagnosis of Federal University of São Paulo UNIFESP 800 Napoleão de Barros St. Vila Clementino CEP São PauloSP04024-002 Brazil

Introduction: Magnetic resonance imaging (MRI) is the most used medical modality for diagnosis and monitoring of multiple sclerosis (MS). A segmentation process is an important task to quantify lesion and its progression. However, manual segmentation of 3D images is tedious, time-consuming, and often not reproducible. The state of the art presents results with room for improvements. Consequently, a semiautomatic segmentation process is proposed and described in this study. Methods: The method consists on a 3D segmentation semiautomatic process for MS lesions in MRI. It initiates by firstly carrying out a preprocessing stage;thus, contrast adjustment is applied to enhance sclerosis regions from other brain information. Secondly, a feature extraction block based on fuzzy connectedness is performed so as to isolate sclerosis lesions from other brain regions. Finally, 3D brain reconstruction is executed along with sclerosis to provide a useful 3D information. Results: The robustness of this approach is demonstrated by high correlation between the results and their corresponding gold standard. The results were also obtained by computing parameters of accuracy of image segmentation, as well as overlap Dice. The proposed method reached true positive of 75.61%, false positive of 16.37%, and DICE of 78.23%. Conclusion: The high correlation between specialist and proposed approach outcome, a better monitoring of the disease, is provided;the specialist can understand the patient’s symptoms, thereby increasing the patient’s quality of life. © 2020, The Author(s).

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Corona discharge-driven platform for precise manipulation of high-viscosity dielectric droplets on copper wires

引用

Sensors and Actuators B: Chemical 2025年 442卷

作者： Tang, Qiang Liu, Manfei Tan, Dengqin Wang, Yan Wang, Ziyi Cui, Xiaxia Zhang, Jia-Han Wang, Chengjun Xue, Changguo State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science & Technology Huainan232001 China School of Artificial Intelligence Anhui University of Science and Technology Hefei231131 China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing School of Electronic Information Engineering Inner Mongolia University Hohhot010021 China School of Artificial Intelligence Anhui Polytechnic University Wuhu241000 China School of Materials Science and Engineering Anhui University of Science and Technology Anhui Huainan232000 China

Dielectric fluids, including polymers, oils, and organic solvents, are integral to applications such as microreactors, optical devices, and thermal management systems due to their stability and resistance to evaporation. However, precise manipulation of dielectric droplets, especially those with high viscosity, remains challenging due to low surface tension and small contact angles. This study introduces a novel corona discharge-driven platform designed for the precise formation, transport, and control of dielectric droplets on one-dimensional copper wires. Utilizing dual needle-tip electrodes, the system generates localized electric fields that facilitate non-contact charge deposition, enabling reliable manipulation of droplets with viscosities up to 30,000 cSt. Comprehensive experiments demonstrate synchronized droplet movement with electrode motion, effective handling of complex geometries, and advanced operations such as droplet merging and splitting. Moreover, dielectric droplets are utilized as micro-containers for performing on-droplet chemical and biological assays. The platform also enables efficient aspiration and release of high-viscosity dielectric fluids, surpassing the capabilities of conventional pipette-based methods. These results highlight the platform's robustness, versatility, and strong potential for integration into next-generation microfluidic and lab-on-a-chip systems. © 2025 Elsevier B.V.

关键词： Bioassay

来源：评论

学校读者我要写书评

暂无评论

Parallax attention for unsupervised stereo correspondence learning

arXiv

引用

arXiv 2020年

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Liang, Zhengfa Lin, Zaiping Yang, Jungang An, Wei China The School of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou510275 China The National Key Laboratory of Science and Technology on Blind Signal Processing China

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations. Our PAM integrates epipolar constraints with attention mechanism to calculate feature similarities along the epipolar line to capture stereo correspondence. Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks. Moreover, we introduce a new and large-scale dataset named Flickr1024 for stereo image super-resolution. Experimental results show that our PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner. Comparative results show that our PASMnet and PASSRnet achieve the state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：