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International Conference on Wireless communications and Signal processing (WCSP)

作者： Jingjing Fan Feng Liu Weibin Bao Hongfei Xia Image Processing and Image Communication Jiangsu Key Laboratory Nanjing University of Posts and Telecommunications Nanjing China Zhijiang College Zhejiang University of Science and Technology Hangzhou China ZTE Telecom Company Limited Nanjing China

Disparity estimation is an important technique in stereo video coding. This paper presents a disparity estimation algorithm based on edge detection. The algorithm makes full use of the human visual characteristics, that is, the human eye is more sensitive to the distortion of the edge region. Therefore, joint estimation is used for edge detection. The large code block size for coding the background region and the flat areas while small size for coding the edge region were used in this paper. Compared to the disparity estimation algorithm proposed in, the proposed algorithm can greatly improve the encoding speed of stereo video without affecting subjective image quality.

关键词： Video coding image coding image edge detection Humans Motion estimation Educational institutions Scalability Redundancy image processing image communication

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： image reconstruction

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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On Wireless Local Area Networks Network Traffic

On Wireless Local Area Networks Network Traffic

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Chinese Control Conference (CCC)

作者： Rong Hong Liang Yiming Wu Ronghui Xu Cheng Yu Fe College of Electrical and Information Engineering Hunan University Changsha China College of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Image Processing & Imagecom Communication Nanjing University of Posts and Telecommunications Nanjing China

Through a series of real tests, this paper analyses the characteristic of IEEE 802.11 traffic, explains the results of the tests. More importantly, the results indicate that the characteristic of IEEE 802.11 traffic exhibits obviously self-similarity. As the first step to set up a 802.11 traffic math-model based on self-similarity theory.

关键词： Wireless LAN Telecommunication traffic Traffic control Educational institutions Testing communication system traffic control image processing Telecommunication computing

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MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

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Medical image analysis 2025年 103卷 103606页

作者： Fenghe Tang Bingkun Nian Yingtai Li Zihang Jiang Jie Yang Wei Liu S Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 PR China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu 215123 PR China Center for Medical Imaging Robotics and Analytic Computing & LEarning (MIRACLE) Suzhou Institute for Advanced Research USTC Suzhou Jiangsu 215123 PR China. Department of Automation Shanghai Jiao Tong University Shanghai PR China Institute of Image Processing and Pattern Recognition Shanghai PR China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai PR China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai PR China. Electronic address: weiliucv@***. Center for Medical Imaging Robotics and Analytic Computing & LEarning (MIRACLE) Suzhou Institute for Advanced Research USTC Suzhou Jiangsu 215123 PR China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Suzhou Jiangsu 215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei Anhui 230026 China. Electronic address: skevinzhou@***.

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM.

关键词： Masked image modeling Medical image pre-training Medical image segmentation State space token interpolation

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Vehicle Color Recognition Using Monocular camera

Vehicle Color Recognition Using Monocular camera

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Mengjie Yang Xiuchang Zhu Liang Li Guang Han Xiaofei Li Jiangsu Provincial Key Lab of Image Processing and ImageCommunication Nanjing Univ. of P&TNanjing Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Ed Engineering Research Center of Wideband WirelessCommunication and Display Technique Ministry ofEduc

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle.

关键词： vehicle color recognition region choice HSV space vehicle detection

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Predicting the pathological status of mammographic microcalcifications through a radiomics approach

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Intelligent Medicine 2021年第3期1卷 95-103页

作者： Min Li Liyu Zhu Guangquan Zhou Jianan He Yanni Jiang Yang Chen School of Biological Science and Medical Engineering Southeast UniversityNanjingJiangsu 210096China Department of Radiology the First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210029China Department of Radiology the Second Affiliated Hospital of Nantong UniversityNantongJiangsu 226001China The State Key Laboratory of Bioelectronics Southeast UniversityNanjingJiangsu 210096China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China The Laboratory of Image Science and Technology Key Laboratory of Ministry of EducationSchool of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China

Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January ***,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics *** Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training *** optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing *** The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

关键词： Nonpalpable microcalcifications Radiomics Machine learning

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One to multiple mapping dual learning: Learning multiple sources from one mixed signal

arXiv

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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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Detecting Optic Disk Based on Structured Learning

Detecting Optic Disk Based on Structured Learning

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IEEE International Conference on Robotics and Biomimetics

作者： Zhun Fan Yibiao Rong Xinye Cai Wenji Li Huibiao Lin Zefeng Yu Jiewei Lu Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial College of Engineering Shantou University. 515063 Shantou China College of Engineering Shantou University. 515063 Shantou China school of computer science and technology Nanjing University of Aeronautics and Astronautics Jiangsu China

ISBN: (纸本)9781467396769

Optic Disk (OD) detection plays an important role for fundus image analysis. In this paper, we propose an algorithm for detecting OD mainly based on a classifier model trained by structured learning. Then we use the model to achieve the edge map of OD. Thresholding is performed on the edge map to obtain a binary image. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on the public database and obtained promising results. The results (an area overlap and Dices coefficients of 0.8636 and 0.9196, respectively, an accuracy of 0.9770, and a true positive and false positive fraction of 0.9212 and 0.0106) show that the proposed method is a robust tool for the segmentation of OD and is very competitive with the stage-of-the-art methods.

关键词： algorithm detecting method

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhan Tong Zhan Wu Yang Yang Weilong Mao Shijie Wang Yinsheng Li Yang Chen Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Laboratory of Image Science and Technology School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.

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