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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

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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Integrating HL7 FHIR into Clinical Decision Support Systems: A Real-World Application with Pepper Humanoid Robot in Hospital During Doctor Visits

Integrating HL7 FHIR into Clinical Decision Support Systems:...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Valentino Šafran Samo Horvat Bojan Ilijevec Igor Rober Roj Vojko Flis Izidor Mlakar Laboratory of Digital Signal Processing University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia Department of Thoracic Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Abdominal and General Surgery University Medical Centre Maribor Maribor Slovenia Department of Nurses in Clinic for Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Cardiac Surgery University Medical Centre Maribor Maribor Slovenia

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

This study explores the integration of HL7 Fast Healthcare Interoperability Resources (FHIR) into a Clinical Decision Support System (CDSS) utilizing a Pepper robot to enhance doctor visits in a hospital setting. We evaluate a real-world implementation where the Pepper robot acts as an interface for data collection from patients, clinical monitors, and hospital information systems. The CDSS leverages FHIR to seamlessly integrate this data and present it visually, aiming to improve clinical decision-making processes for doctors. Our research investigates the potential of this FHIR-based CDSS with a robot interface to optimize healthcare delivery and potentially improve patient outcomes.

关键词： Decision support systems Surveys Hospitals Service robots Soft sensors Transforms User experience Usability Monitoring Interoperability

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signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Signal structure information-based data association for mane...

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IEEE International Conference on Radar

作者： Chang Gao Hongwei Liu Pramod K. Varshney Junkun Yan Penghui Wang National Laboratory of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Syracuse University NY USA

ISBN: (纸本)9781665468893

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.

关键词： Correlation Convolution Simulation Radar Euclidean distance Transforms Radar tracking

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The PHD/CPHD filter for multiple Extended Target Tracking with Trajectory Set Theory and Explicit Shape Estimation

arXiv

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arXiv 2025年

作者： Cheng, Yuanhao Cao, Yunhe Yeo, Tat-Soon Jie, Fu Zhang, Wei National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extent shape. These two methods are deduced from the famous Probability Hypothesis Density (PHD) filter and the Cardinality-PHD (CPHD) filter, respectively. In these two methods, Trajectory Set Theory (TST) is combined to establish the target trajectory estimates. Moreover, by employing a decoupled shape estimation model, the proposed methods can explicitly provide the shape estimation of the target, such as the orientation of the ellipse extension and the length of its two axes. We derived the closed Bayesian recursive of these two methods with stable trajectory generation and accurate extent estimation, resulting in the TPHD-E filter and the TCPHD-E filter. In addition, Gaussian mixture implementations of our methods are provided, which are further referred to as the GM-TPHD-E filter and the GM-TCPHD-E filters. We illustrate the ability of these methods through simulations and experiments with real data. These experiments demonstrate that the two proposed algorithms have advantages over existing algorithms in target shape estimation, as well as in the completeness and accuracy of target trajectory generation. Copyright © 2025, The Authors. All rights reserved.

关键词： Group theory

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Data Fusion for multipath-Based SLAM: Combining Information from multiple Propagation Paths

arXiv

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arXiv 2022年

作者： Leitinger, Erik Venus, Alexander Teague, Bryan Meyer, Florian The Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria The Christian Doppler Laboratory for Location-aware Electronic Systems Austria The MIT Lincoln Laboratory LexingtonMA United States The Department of Electrical and Computer Engineering and Scripps Institution of Oceanography University of California San Diego San DiegoCA United States

multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor localization constrained by limited navigation resources. The goal of multipath-based SLAM is to support the estimation of time-varying positions of mobile agents by detecting and localizing radio-reflective surfaces in the environment. In existing Bayesian methods, a propagation surface is represented by the mirror image of each physical anchor (PA) across that surface – known as the corresponding "virtual anchor" (VA). Due to this VAs representation, each propagation path is mapped individually. Existing methods thus neglect inherent geometrical constraints across different paths that interact with the same surface, which limits accuracy and speed. In this paper, we introduce an improved statistical model and estimation method that enables data fusion in multipath-based SLAM. By directly representing each surface with a master virtual anchor (MVA), geometrical constraints across propagation paths are also modeled statistically. A key aspect of the proposed method based on MVAs is to check the availability of single-bounce and double-bounce propagation paths at potential agent positions by means of ray-tracing (RT). This availability check is directly integrated into the statistical model as detection probabilities for propagation paths. Estimation is performed by a sum-product algorithm (SPA) derived based on the factor graph that represents the new statistical model. Numerical results based on simulated and real data demonstrate significant improvements in estimation accuracy compared to state-of-the-art multipath-based SLAM methods. © 2022, CC BY.

关键词： Mobile agents

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Automatic Modulation Classification of Frequency-Hopping signals Using High-dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

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2023 IEEE International Symposium on Broadband multimedia Systems and Broadcasting, BMSB 2023

作者： Chen, Qibo Yan, Kun Wu, Hsiao-Chun Chang, Shih Yu Yan, Xiao Wu, Yiyan Chang, Haonan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing GuilinGuangxi China Guilin University of Electronic Technology Department of Information and Telecommunication Guangxi Guilin541004 China Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States San Jose State University Department of Applied Data Science San JoseCA95192 United States University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China Communications Research Centre Canada OttawaK2H 8S2 Canada Haonan Chang Is with Xi'an University Xi'an China

ISBN: (纸本)9798350321524

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM);the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise. © 2023 IEEE.

关键词： Support vector machines

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Novel Personalized multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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IEEE International Symposium on Broadband multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Xinjia Chen Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin China Department of Engineering Technology Northwestern State University of Louisiana Natchitoches LA USA Communications Research Centre Canada Ottawa ON Canada

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users’ preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user’s on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems.

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Variability in Spread Spectrum Time Domain Reflection and Transmission Measurements

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Makin, Samuel Addad, Mouad Redhead, Brent Furse, Cynthia M. University of Utah Electrical and Computer Engineering Department Salt Lake City 84112 UT United States Djillali Liabes University of Sidi Bel Abbes Telecommunications and Digital Signal Processing Laboratory SBA Algeria

Spread Spectrum Time Domain Reflectometry (SSTDR) offers a new non-intrusive measurement technique for microwave imaging. While vector network analyzers (VNAs) are conventional tools in this domain, they present significant drawbacks such as high cost and lengthy scan times. This paper explores the viability of substituting SSTDR devices for the VNA by validating their accuracy in comparison to traditional VNAs in the 0.5-4 GHz band. We conducted several tests of reflection and transmission measurements of RC loads, analysis of variations due to cable movement, calibration consistency, and temperature-induced variations. We also measured antenna S11 and S21 in two oil-based phantoms to simulate human breast tissue, comparing changes with the insertion of a metal tumor substitute. We found the mean confidence intervals of the VNA and SSTDR are better (less) than 0.03 for the |S11| and 0.02 for the |S21| from 0.5-4 GHz, and that metal “tumors” in the oil are detectable. These results suggest that SSTDR may be a viable alternative to VNAs for applications such as microwave breast imaging, warranting further investigation in more robust setups.

关键词： antenna measurements reflectometry spread spectrum time domain reflectometry (SSTDR) variability

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