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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

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image Fusion Algorithm Based On Orientation Information Motivated Pulse Coupled Neural Networks

Image Fusion Algorithm Based On Orientation Information Moti...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.6

作者： Xiaobo Qu Changwei Hu Jingwen Yan Department of Communication Engineering Xiamen University Xiamen Fujian China Department of Software Engineering Xiamen University Xiamen Fujian China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou Guangdong China

Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.

关键词： image fusion Pulse Coupled Neural Networks Orientation information Wavelet transform image processing

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Adaptive mutation particle filter based on diversity guidance

Adaptive mutation particle filter based on diversity guidanc...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin-Xia Yu Yong-Li Tang Wen-Jing Liu College of Computer Science and Technology Henan Polytechnic University Jiaozuo China Jiangsu Provincial Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

Aimed at the deficiency of the resampling algorithm in PF, diversity measures ESS (effective sample size) and PDF (population diversity factor) are evaluated respectively. Combined with the estimation result, diversity measures PDF is used for adaptively tuning the resampling threshold. By integrating the operation of particle mutation after resampling into PF and using the above mechanism of diversity guidance, the AMPF algorithm (Adaptive Mutation PF) is presented so as to assure the diversity of particle sets. With the simulation program using matlab 7.0 to track a single target motion from a fixed visual observation points, the performance of diversity measures and AMPF are evaluated and the validity of the proposed method is verified.

关键词： Particle measurements Atmospheric measurements Particle filters Algorithm design and analysis Mathematical model Tuning Estimation

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Transclaw U-net: Claw U-net with transformers for medical image segmentation

arXiv

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arXiv 2021年

作者： Yao, Chang Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Ryerson University

In recent years, computer-aided diagnosis has become an increasingly popular topic. Methods based on convolutional neural networks have achieved good performance in medical image segmentation and classification. Due to the limitations of the convolution operation, the long-term spatial features are often not accurately obtained. Hence, we propose a TransClaw U-Net network structure, which combines the convolution operation with the transformer operation in the encoding part. The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling. The transformer part is used to encode the patches, and the self-attention mechanism is used to obtain global information between sequences. The decoding part retains the bottom upsampling structure for better detail segmentation performance. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. The ablation experiments also prove the generalization performance of TransClaw U-Net. © 2021, CC BY.

关键词： Convolution

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Real-time foreground detection based on tempo-spatial consistency validation and Gaussian Mixture Model

Real-time foreground detection based on tempo-spatial consis...

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

作者： Qing Yan Yi Xu Xiaokang Yang Leonardo Traversoni Institute of Image Communication and Information Processing and Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China Ciencias Basicas e Ingenieria University of Autonoma Metropolitana Iztapalapa Mexico

ISBN: (纸本)9781424444618

Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.

关键词： image communication Information processing Gaussian processes Video sequences Colored noise Switches Lighting Probability Kernel Optical sensors

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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A Novel Group-based Swarm Optimizer for Large-Scale Optimization

A Novel Group-based Swarm Optimizer for Large-Scale Optimiza...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shanwen Guan Rushi Lan Yijie Zhu Ruomei Wang Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.

关键词： Optimization Convergence Graphics Space exploration Sociology Statistics Next generation networking

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An Adaptive Fractional Pixel Search Algorithm

An Adaptive Fractional Pixel Search Algorithm

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International Conference on Intelligent Sensing and Information processing (ICISIP)

作者： Liquan Shen Zhaoyang Zhang Zhi Liu Wenjun Zhang Key Laboratory of Advanced Display and System Application Ministry of Education Shanghai University Shanghai China Institute of Image Communication& Information Processing Shanghai Jiaotong University China

In this paper, we propose an adaptive fractional pixel search algorithm for reduction computation its load. Based on that SAD (sum of absolute difference) error surface is unimodal within the range of plusmn1 pixel, a novel fractional pixel search bypass strategy is first proposed; Then, a strategy of fractional pixel search early termination based on all-zero block detection is proposed. Finally, an adaptive fractional pixel search algorithm adopting above strategies and improved CBFPS (center biased fractional pixel search) algorithm is proposed. Compared with the fractional pixel full search algorithm and JVT-F017 that adopts CBFPS, simulation results show that the proposed algorithm can reduce 76.60% and 68.22% fractional pixel search points respectively while it still maintains similar coding efficiency.

关键词： Motion estimation Vectors Video coding Testing Paramagnetic resonance Pixel image communication Information processing Computer displays Redundancy

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Renal 4D CEST-MRI under free breathing

Renal 4D CEST-MRI under free breathing

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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Tao, Quan Chen, Zelong Zhang, Yizhe Zhang, Wenyan Xu, Yikai Feng, Yanqiu Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China School of Biomedical Engineering Southern Medical University Guangzhou China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Medical Imaging Center Nanfang Hospital Southern Medical University Guangzhou China

ISBN: (纸本)9781510682825

Introduction: Respiration-induced motion can lead to artifacts in chemical exchange saturation transfer (CEST) images, and inaccurate quantization of CEST signals. The aim of this study was to develop a motion-insensitive renal 4D (extra motion dimension) CEST imaging technique for clinical use during free breathing. Methods: We utilized a 3D turbo filed echo (TFE) sequence with in-plane golden-angle radial sampling and out-plane Cartesian sampling (stack of stars) to acquire renal CEST images from three normal volunteers under free breathing. The respiration states were separated, and XD-GRASP was used to reconstruct the CEST images. The efficacy of this method in suppressing motion artifact was evaluated. Results: The renal Z-spectrum demonstrated distinct CEST peaks, and no motion artifacts were observed in the CEST images. This motion-insensitive sequence proved to be reliable for renal CEST imaging. Conclusion: 4D CEST-MRI provides a motion in-sensitive CEST imaging approach for renal imaging in clinical nephropathy patients under free breathing, this may improve the accuracy of nephropathy detection. © 2024 SPIE.

关键词： Magnetic resonance imaging

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Performance Bound of Position Estimation in image Matching

Performance Bound of Position Estimation in Image Matching

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2011 International Conference on Advanced Materials in Microwaves and Optics（AMMO 2011）

作者： Bao-shu XU Ze-lin SHI Shenyang Institute of Automation Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Key Laboratory of Image Understanding and Computer Vision Liaoning Province

Position estimation in image matching is a fundamental step in computer vision and image processing. To deal with the problem of performance prediction, we formulate it under statistical parameter estimation aspect. The lower bound of position estimation variance is obtained based on Cramer-Rao lower bound(CRLB) theory. This paper analyses the impact of noise to 1-D signal matching, derives the lower bound of variance, and then extends it to 2-D image matching. Furthermore, we derive numerical expression that can be computed from observed data. Finally, we use Monte Carlo simulation method to verify the derived analytical expressions. Experimental results show that the derived CRLB is tight to simulation estimated variance. The CRLB can characterize the performance bound of position estimation in image matching.

关键词： Performance prediction image matching Cramer-Rao Lower Bound Fisher information

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