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Study on Prostate image Segmentation Using Improved U-NET

Study on Prostate Image Segmentation Using Improved U-NET

引用

international conference on image, Vision and Intelligent Systems, ICIVIS 2021

作者： Sun, Mengya Department of Mathematical Sciences University of Electronic Science and Technology of China Chengdu610000 China

ISBN: (纸本)9789811669620

Medical image plays a key role in the analysis and treatment of the patient’s condition in today’s medicine, but the clinical processing of medical image is still largely dependent on the subjective experience of doctors, and the process of manual extraction of information is time-consuming and labor-consuming. With the development of deep learning, it has become a trend to apply deep learning to medical image processing. However, due to the characteristics of medical images, many advanced segmentation algorithms fail to achieve good results in medical images. In addition, medical image data generally have the problem of insufficient or too small data sets, and there are few ways to obtain samples, mainly from some hospitals and medical institutions. Besides, the labeling work is not competent for ordinary people. So how to realize the automatic analysis and processing of medical images has always been a hot topic in the field of computer science. In this paper, we designed an experiment of prostate segmentation algorithm, built a model using a U-Net network that performs well in the field of medical image segmentation, and tried to improve its performance by combining a variety of structures, such as ASPP, ResNext, and attention module. The results show that our proposed model achieves better segmentation performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Innovation in Electrical Power Engineering, Communication, and Computing Technology 1

引用

丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Manohar Mishra Renu Sharma Akshay Kumar Rathore Janmenjoy Nayak Bighnaraj Naik

ISBN: (数字)9789811670763

ISBN: (纸本)9789811670756;9789811670787

This book features selected high-quality papers from the Second international conference on Innovation in Electrical Power Engineering, Communication, and Computing Technology (IEPCCT 2021), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on 24–26 September 2021. Presenting innovations in power, communication, and computing, it covers topics such as mini, micro, smart and future power grids; power system economics; energy storage systems; intelligent control; power converters; improving power quality; signal processing; sensors and actuators; image/video processing; high-performance data mining algorithms; advances in deep learning; and optimization methods.

关键词： Energy Systems signal, image and Speech processing Circuits and Systems Professional Computing Power Electronics, Electrical Machines and Networks

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Gesture Recognition and Control based on Visible Light Communication 43

Gesture Recognition and Control based on Visible Light Commu...

引用

43rd Chinese Control conference, CCC 2024

作者： Liang, Xiaojun Li, Jiarong Xu, Chihan Xie, Zixuan Zhang, Chaobo Ding, Wenbo Gui, Weihua Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Central South University School of Automation Changsha410083 China

ISBN: (纸本)9789887581581

The evolution of human-computer interaction (HCI), particularly in the era of the Internet of Things (IoT), demands innovative solutions that transcend traditional interfaces like keyboards and touchscreens. This study introduces a visible light communication (VLC) based sensing system for non-contact gesture recognition in HCI. The system utilizes a cost-effective VLC setup optimized for indoor environments, ensuring precise signal transmission and reception. The data acquisition and processing board can accurately capture and analyze light-based hand gesture signals. This process is supported by a comprehensive signal processing framework that includes data preprocessing steps and robust machine learning algorithms. These algorithms are designed to detect subtle light pattern variations due to gestures, ensuring an accurate and responsive recognition process. The system's effectiveness is confirmed by its high gesture recognition accuracy of 95.7%. The study's key contributions include the development of a VLC-based system for gesture recognition and control, the integration of diverse signal processing techniques suitable for VLC-based data, and the successful demonstration of the system's practical application through extensive experimental evaluations. These evaluations have validated the system's efficacy in real-world scenarios, marking an advancement in VLC-enabled HCI. The research highlights VLC's potential as a user-friendly and efficient interface in HCI, offering more intuitive and responsive interactions in smart environments. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Gesture recognition

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Height Estimation from a Single SAR image by Depth-Aware Proxyless Neural Architecture Search 7

Height Estimation from a Single SAR Image by Depth-Aware Pro...

引用

7th international conference on signal and image processing, ICSIP 2022

作者： Minglong, Xue Jian, Li Qingli, Luo Tianjin University State Key Laboratory of Precision Measuring Technology and Instruments Tianjin China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Height estimation can be estimated from a single synthetic aperture radar (SAR) image via an advanced U-shaped convolution neural network (CNN)-Unet. Manually designed architectures are applied to achieve high reconstruction accuracy. Neural Architecture Search (NAS) is efficient for designing the architecture of convolution neural network (CNN) automatically for a specific task, but it is difficult to be deployed directly due to the limitations of computational resources. Then, proxy tasks are developed for traditional NAS methods. However, the trained model for proxy tasks is not ptimal for the target task. In this paper, we propose a depth-aware penalty proxyless NAS method for Unet (DPNAS-Unet) to estimate the height from a single SAR image. Symmetric residual blocks are proposed to improve the representation capability of the model. A depth-aware architecture loss is proposed for optimal architecture learning. Supplementary sparse height map is included as inputs of our model to improve the performance. In the end, comparison experiments are performed to show the superiority of PDNAS-Unet. © 2022 IEEE.

关键词： Synthetic aperture radar

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Pareto Optimal Approach for Contrast Adjustment in Gray Level images Using IDS Algorithm 1

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international conference on Communications, signal processing and VLSI, IC2SV 2019

作者： Kotte, Sowjanya Warangal Telengana State506015 India

ISBN: (数字)9789813340589

ISBN: (纸本)9789813340572

image enhancement/contrast adjustment plays an important role in almost every image processing system. The main aim of the contrast adjustment is to enhance image quality by maximizing the information content in the image. Many researchers implemented heuristic approaches for image enhancement using maximization of objective function based on image quality metrics. And such objective function may fail to yield proper enhancement for a given image (either under enhanced or over enhanced). Because the parameters of the image transformation function are selected based on overall value of the objective function but not individual objectives in the objective function. That means the parameter values so chosen may not satisfy all the objectives of the objective function, and this may lead to improper enhancement of the image. In this context, this paper presents a Pareto optimal approach-based gray level contrast adjustment using Improved Differential Search Algorithm (IDSA) named Multi-Objective Improved Differential Search Algorithm (MOIDSA). image enhancement is treated as a three-dimensional optimization problem, and MOIDSA is used to solve it. The input image quality is enhanced by simultaneous objectives which is a blend of image performance measures and quality metric as a three-dimensional optimization approach will yield best-compromised solution by satisfying all the individual objectives. This algorithm is tested on some standard test gray level images, and results compared with well existing algorithms have proven its superiority. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Carrier Robust Reversible Watermark Model Based on image Block Chain Authentication 11th

Carrier Robust Reversible Watermark Model Based on Image Blo...

引用

11th international conference on image and Graphics, ICIG 2021

作者： Gao, Yating Zhang, Ru Li, Xianxu Liu, Jianyi Zhao, Kaifeng Cheng, Xue State Grid Information and Telecommunication Branch Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030873547

Digital watermarking technology protects information security from the dimension of the existence of hidden information. At present, most watermarking algorithms only focus on the embedding and extraction of secret information, which usually brings permanent distortion to the carrier. This poses many challenges in scenarios that require high data authentication, such as medical image processing and military communications. This paper proposes a robust reversible watermarking algorithm for carriers based on block chain authentication. It is designed to mark the tampered location of the carrier with a fragile watermark, and restore the tampered information through authentication before and after, so that the peak signal-to-noise ratio of the restored carrier can be greatly improved when tampering occurs. The experimental results show that the scheme has good recovery ability in the scene of tampering with the watermark image. © 2021, Springer Nature Switzerland AG.

关键词： Medical imaging

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8th international conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

8th International Conference on Scale Space and Variational ...

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8th international conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

ISBN: (纸本)9783030755485

The proceedings contain 45 papers. The special focus in this conference is on Scale Space and Variational Methods in Computer Vision. The topics include: Multiscale Registration;challenges for Optical Flow Estimates in Elastography;an Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation;low-Rank Registration of images Captured Under Unknown, Varying Lighting;towards Efficient Time Stepping for Numerical Shape Correspondence;first Order Locally Orderless Registration;first-Order Geometric Multilevel Optimization for Discrete Tomography;bregman Proximal Gradient algorithms for Deep Matrix Factorization;Hessian Initialization Strategies for -BFGS Solving Non-linear Inverse Problems;inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting;quantisation Scale-Spaces;A Scaled and Adaptive FISTA Algorithm for signal-Dependent Sparse image Super-Resolution Problems;Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI;wasserstein Generative Models for Patch-Based Texture Synthesis;sketched Learning for image Denoising;Translating Numerical Concepts for PDEs into Neural Architectures;CLIP: Cheap Lipschitz Training of Neural Networks;variational Models for signal processing with Graph Neural Networks;synthetic images as a Regularity Prior for image Restoration Neural Networks;geometric Deformation on Objects: Unsupervised image Manipulation via Conjugation;learning Local Regularization for Variational image Restoration;Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations;on the Correspondence Between Replicator Dynamics and Assignment Flows;learning Linear Assignment Flows for image Labeling via Exponential Integration;on the Geometric Mechanics of Assignment Flows for Metric Data Labeling;a Deep image Prior Learning Algorithm for Joint Selective Segmentation and Registration.

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Make the most of MUST, an open-source Matlab UltraSound Toolbox

Make the most of MUST, an open-source Matlab UltraSound Tool...

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IEEE international Ultrasonics Symposium (IEEE IUS)

作者： Garcia, Damien Univ Lyon 1 INSA Lyon CREATIS Ctr Rech Acquisit & Traitement Image Sant CNRS UMR 5220INSERM U1294INSERM Lyon France Univ Montreal CRCHUM Ctr Rech Ctr Hosp LBUM RUBIC Res Unit Biomech & Imaging Cardiol Montreal PQ Canada

ISBN: (纸本)9780738112091

Medical ultrasound imaging requires recurring use of elementary principles, namely, the choice of the transducer, the design of the emitted waves and their wavefronts (e.g. focused or wide), the demodulation of the received signals, their beamforming, and their post-processing to generate B-mode or flow images. The ultrasound signals used for research purposes can be synthetic or acquired. To make the whole pipeline easily accessible to many researchers and students, the objective was to provide an open-access toolbox, widely documented, and adapted to ultrasound imaging research involving experimental or simulation methods. The MUST Matlab UltraSound Toolbox contains algorithms that focus on the development, simulation, and analysis of ultrasound signals for medical imaging. The user can design various ultrasound-imaging scenarios and analyze their performance using simulated or acquired data. The MUST functions allow studying the characteristics of transducers and waveforms, analyze signals, and construct ultrasound images. The many examples provide a starting point for students and researchers to quickly gain an understanding of the essentials of ultrasound imaging. The simulators integrated into MUST provide very realistic acoustic pressure fields and ultrasound images. The Matlab MUST toolbox is freely available at https://***/MUST. Before engaging in advanced ultrasound techniques and comparing them with so-called standard methods, it is necessary to have a good understanding of the latter and their advantages and limitations. With this in mind, the MUST toolbox includes everything needed for comprehensive ultrasound imaging: simulators of acoustic pressure fields and backscattered RF signals, delay-and-sum beamforming, B-mode imaging, wall filtering, color or vector Doppler, speckle tracking... The documentation and open access facilitate easy and intuitive use. If the MUST toolbox proves to be interesting, the author plans to integrate advanc

关键词： Ultrasonic imaging Medical simulation signal processing image processing Open access

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Fast ART Algorithm Based on Simplified Weighting Factor Calculation 22

Fast ART Algorithm Based on Simplified Weighting Factor Calc...

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7th international conference on Biomedical signal and image processing, ICBIP 2022

作者： Wang, Xianchao Zhang, Yong Dai, Yu School of Information Engineering Chaohu University Hefei238024 China Zhixing College Hubei University Wuhan432100 China

ISBN: (纸本)9781450396691

In the image reconstruction of computed tomography (CT), the algebra reconstruction technique (ART) has many virtues that the analytic reconstruction algorithms don't have. In this paper, a simplified weighting factor calculation method is proposed. All voxels are firstly projected at a scanning angle, and the voxels and the rays are classified on the basis of the distance between the voxel's projection and the ray's projection. For the reason that the voxel mainly contributes to the projection value of the nearest ray, the weighting factors of the voxels to the ray's projection value in the same classification is approximately 1. Then the reconstruction equations are obtained at a scanning angle. Finally, based on the proposed simplified weighting factor calculation method, a fast ART algorithm for CT image reconstruction is developed. The simulation results show the validity of the proposed fast ART algorithm. © 2022 ACM.

关键词： image reconstruction

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Pu-Edgeformer++: An advanced Hierarchical Edge Transformer for Arbitrary-Scale Point Cloud Upsampling using Distance Fields 49

Pu-Edgeformer++: An Advanced Hierarchical Edge Transformer f...

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2024 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2024

作者： Kim, Dohoon Shin, Minwoo Ryu, Jaeseok Lim, Heunseung Paik, Joonki Chung-Ang University Department of Image Seoul Korea Republic of Chung-Ang University Department of Artificial Intelligence Seoul Korea Republic of

Despite of pre-processing the raw point cloud is important, limited research has been conducted on learning-based approaches to point cloud upsampling. PU-EdgeFormer [1] model stands out for its exceptional performance, which is attributed to its unique inductive biases that seamlessly blend both local and global characteristics of point clouds through the integration of graph convolution and transformer mechanisms. In this paper, we present EdgeFormer++, an advanced hierarchical edge transformer module designed for arbitrary-scale point cloud upsampling. Our module employs crossattention and dense connections to integrate information from both feature and input point cloud while maintaining the structural elements of graph convolutions and transformers. Experimental results, both qualitative and quantitative, indicate that our proposed method outperforms existing techniques in point cloud upsampling. The official source code is accessible at https://***/dohoon2045/PU-EdgeFormer2. © 2024 IEEE.

关键词： graph convolution implicit neural representation Point cloud upsampling vision transformer

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