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Enhancing Low-Dose CT image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism

Enhancing Low-Dose CT Image Quality Through Deep Learning: A...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Zubair, Muhammad Rais, Helmi B Md Ullah, Fasee Yousafzai, Arsalaan Khan Hassan, Farrukh Universiti Teknologi Petronas Department of Computer and Information Sciences Seri Iskandar32610 Malaysia Universiti Teknologi Petronas Department of Civil and Enviromental Engineering Seri Iskandar32610 Malaysia Sunway University Bandar University Sunway Department of Computing and Information Systems School of Engineering and Technology Selangor Darul Ehsan 47500 Malaysia

ISBN: (纸本)9798350372229

Computed Tomography (CT) scans utilize electromagnetic radiation. However, the excessive exposure of a patient's body during CT acquisition poses potential health risks. Subsequently, the integration of low-dose CT scans has resulted in an escalation of noise, artifacts, and a discernible decline in the overall quality of CT imaging, significantly impacting the diagnostic capabilities of Computer-Aided Diagnosis (CAD) system. Removing these noises and artifacts while preserving critical information is a challenging task. Traditional noise reduction algorithms are expensive, produce blurry results, and rely on challenging sinogram data. As a result, deep learning-based image-denoising approaches have emerged. This study introduces a DoG-UNet+ model which incorporates a novel layer called the 'Difference of Gaussians (DoG) Sharpening Layer' into the U-Net architecture. This layer utilizes two distinct convolutional kernels, termed 'fat' and 'skinny,' aimed to capturing diverse scales of features. To increase the clinical detection precision, an attention mechanism has been added to focus on the critical features in CT images. This model has been compared with the most recent algorithms based on the Peak signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE), and Structural Similarity Index (SSIM). The outcomes showed superior performance of DoG-UNet+ model, showcasing promising and notable results compared to existing methods. © 2024 IEEE.

关键词： Computed Tomography Deep Learning image Enhancement LDCT Noise Removal

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A Review on Content based image Retrieval Techniques

A Review on Content Based Image Retrieval Techniques

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2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Suresh Kumar, J.S. Maria Celestin Vigila, S. Noorul Islam Centre for Higher Education Kumaracoil Department of Computer Science and Engineering Tamilnadu Kanyakumari India Noorul Islam Centre for Higher Education Kumaracoil Department of Information Technology Tamilnadu Kanyakumari India

ISBN: (纸本)9798350333244

Significant interest in Content-based image Retrieval (CBIR) has been identified over the last two decades, which utilizes image and video perception to find adequate images. The availability of graphical and multimedia data, as well as the expansion of the internet, demands field matching. Because of the lack of visual content as a ranking clue, methodologies for visual retrieval that use text search methods may suffer from lack of consistency between full texts and visual content. Many programmers and tools have been developed to help users develop, implement, and conduct research on visual or audio content, as well as to browse large multimedia repositories. A benchmark for transmission model has been established DICOM, and patient data can be ended up saving along with the images, although there are still a few issues with standardization. Several papers have proposed content-based access to medical images to aid clinical decision-making and simplify medical evidence, and instances for integrating based on their content access methods into images and communication systems have been developed. This work provides a thorough evaluation of current advancements in the fields of CBIR. This investigation utilizes key characteristics of numerous retrieval methods for images and frameworks, ranging from basic identification of features from images to current deep-learning approaches. © 2023 IEEE.

关键词： Hospital data processing

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2nd international conference on Sustainable technology and Advanced Computing in Electrical Engineering, ICSTACE 2023

2nd International Conference on Sustainable Technology and A...

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2nd international conference on Sustainable technology and Advanced Computing in Electrical Engineering, ICSTACE 2023

ISBN: (纸本)9789819992348

The proceedings contain 40 papers. The special focus in this conference is on Sustainable technology and Advanced Computing in Electrical Engineering. The topics include: Performance Analysis of Various Converters in PV systems Using PSO-based MPPT;Ultra-Gain Two-Input Two-Output DC–DC Converter for Electric Vehicle Application;Modified Switched Capacitor-based Non-isolated Bidirectional DC–DC Converter for Obtaining High VTR;wavelet-based Quadrature Rule to Evaluating Quasi-Type Singular Integral Equations;Spherical Induction Motor—Speed Control Using VFD;a Machine Learning and Fuzzy-based Reliable Data Collection and Communication in AioT—Fog Computing Environment;smart Home Energy Management with a Coordinated Neighborhood Energy Sharing;Dynamic Operation of Islanded DC Microgrid with Fuel Cell Using Hybrid Energy Storage systems;fault Detection, Classification, and Identification of Shunt Compensated Distribution System Through Wavelet Transforms;Optimal Sizing of PV/Wind/Battery Grid-Connected Hybrid Renewable Energy systems Using TLBO Algorithm;diagnosis of Clustered Microcalcifications in Breast Cancer Using Mammograms;Stochastic Optimal Allocation of Photovoltaic-based DG and DSTATCOM in Distribution System Considering Uncertainties of Loads and Solar Irradiance;breast Cancer: Automatic Detection from Mammogram images by Utilizing Deep Learning Methods;Standalone Photovoltaic Fed Water Pumping System with Better Performance Using P&O MPPT Techniques;Accurate Estimation of Amplitude, Phase, and Frequency of a Sinusoidal signal Contaminated with Harmonics and DC Decaying Components;experimental Investigation and Pollution Contaminant Severity Analysis of Glass and Porcelain Insulators Under Controlled Conditions;Classification of Dry and Wet Snow of Glaciers in Western Himalayas Using SAR Polarimetric Decomposition—An Application of Satellite image Processing;optimum Overcurrent Relay Coordination for Radial Distribution Networks Using Improved Mathematical Fo

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A Method for Denoising from Low-Dose CT images based on Noise2Noise 23

A Method for Denoising from Low-Dose CT Images Based on Nois...

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23rd international conference on Control, Automation and systems, ICCAS 2023

作者： Sawada, Shuji Murakami, Seiichi Guangxu, Li Kamiya, Tohru Kyushu Institute of Technology 1-1 Sensui Tobata Fukuoka Kitakyushu804-8550 Japan Junshin Gakuen University 1-1-1 Chikushigaoka Minami Fukuoka Fukuoka815-8510 Japan Tiangong University No.399 BinShuiXi Road XiQing District Tianjin300387 China

ISBN: (纸本)9788993215274

Japan has the highest number of CT scanners per million people among developed countries, and the annual medical radiation dose is about six times higher than the world average. Screening of high-risk populations by means of low-dose CT has been verified to be able to reduce lung cancer mortality. However, the low-dose CT image noise should be accurately estimated in order to achieve good image quality as normal dose CT. In this paper, we apply a denoising method using convolutional neural networks without use of the clean images during learning phase. The CNN model is based on SRResNet, which has achieved high performance in super-resolution tasks, and has two attention mechanisms, Spatial Attention and Channel Attention, in the Residual Block. Experiments using whole body slice CT images of pigs showed that the proposed method is useful by comparison with normal dose CT images and by evaluation of image quality using peak signal-to-noise ratio (PSNR). © 2023 ICROS.

关键词： image quality

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Real-Time Hand signal Detection Using Convolutional Neural Networks

Real-Time Hand Signal Detection Using Convolutional Neural N...

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2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Viswanatha, V. Ramachandra, A.C. Parameshachari, B.D. Chaitra, M. Harika, L. Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350315455

This paper aims at developing a robust hand signal detection system using the popular deep learning framework of TensorFlow. By harnessing the power of convolutional neural networks (CNNs), the study endeavors to achieve real-time, accurate, and efficient recognition of hand signals from video or image data. The dataset serves as the foundation for training a CNN model using TensorFlow. Through the process of training, the model learns to extract discriminative features associated with different hand signals. Various pre-processing techniques, including data augmentation, normalization, and resizing are employed to enhance the model's ability to generalize across different variations of hand signals. The developed hand signal detection system utilizes the trained CNN model for inference. Input video or image data is fed into the model, which analyzes and classifies hand signals based on the learned features. To enhance the real-time performance of the system, optimization techniques such as model quantization and hardware acceleration are explored and implemented. Performance metrics such as accuracy(96.5%), precision(0.91), recall(0.94), and F1-Score(0.92) are measured to assess the effectiveness and efficiency of the developed system and same results are declared in the result section. © 2023 IEEE.

关键词： Convolution Neural Networks Hand Gesture detection

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Analysis on image Compression for Multimedia Communication Using Hybrid of DWT and DCT 1st

Analysis on Image Compression for Multimedia Communication U...

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1st international conference on Microelectronics, Communication systems, Machine Learning, and internet of Things, MCMI 2020

作者： Ahmad, Aamir Junaid Hassan, Syed Danish Priyadarshi, Rahul Nath, Vijay Maulana Azad College of Engineering and Technology Aryabhatta Knowledge University Bihar Patna India Electronics and Communication Engineering Discipline Science Engineering and Technology School Khulna University Khulna9028 Bangladesh Department of Electronics and Communication Engineering National Institute of Technology Patna Patna India Department of Electronics and Communication Engineering Birla Institute of Technology Mesra Ranchi India

ISBN: (纸本)9789811919053

image compression is a technique to compress information that converts the original image with limited bits and reconstructs it with some distortion. This compression leads to native disc memory saving;conjointly, it helps in speedy multimedia system files transfer. This analysis presents a projected technique for the compression of multimedia system pictures victimization hybrid compression technique (discrete trigonometric function remodel and separate moving ridge transform). DWT and DCT Area unit applied initial on individual RGB parts. Once applying this, image is amount for every distinctive amount. Therefore, on verifying the distinctive code for every distinctive image for his or her cryptography, the standard of the new image compression technique is evaluated victimization the height signal to noise magnitude relation (PSNR) through simulations victimization MATLAB. The simulation results show that the projected JPEG codec delivers increased performance over the standard JPEG code in terms of PSNR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image compression

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MTF compensation of baseline adjustable optical sparse aperture imaging system 7

MTF compensation of baseline adjustable optical sparse apert...

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7th Global Intelligent Industry conference, GIIC 2024

作者： Wu, Yong Tao, Tianbao Hui, Mei Liu, Ming Dong, Liquan Kong, LingQin Zhao, Yuejin Anhui Hawto Cable Group postdoctoral research workstation China Beijing Institute of Technology China

ISBN: (纸本)9781510682986

For a single optical sparse aperture imaging system, because of the sub-mirrors’ dispersion and sparsity, the modulation transfer function (MTF) is unlikely to satisfy all frequencies due to inadequate sampling of the discrete sub-apertures. Thus, a baseline adjustable optical sparse aperture imaging system is proposed by expanding the sub-aperture array. Sufficient information from separate frequency regions can be maintained and fused. An improved Wiener filter algorithm based on the system's transfer function and noise features is designed for image restoration. Both the simulation and experiment prove that the proposed method can achieve a satisfactory MTF level and a clear reconstruction effect. The average peak signal to noise ratio is raised from 22.53 dB of degraded images to 29.17 dB of the restored images. The structural similarity index of the results is increased from 0.65 to 0.92. And compared with several conventional algorithms, scores of multiple evaluation indexes of the proposed method is the highest. © 2024 SPIE.

关键词： Modulation transfer functions image restoration Optical transfer functions Imaging systems signal to noise ratio

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Multipurpose internet of Things-based Robot for Military Use 6th

Multipurpose Internet of Things-Based Robot for Military Use

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6th international conference on communication and computational technologies, ICCCT 2024

作者： Linga Varshini, P. Pavithra, P. Jeffin Gracewell, J. Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819774227

Creating intelligent robots, such as those used to provide excellent customer service, is a huge step forward for society in many ways. Robots are slowly taking over many workplaces with their frequent replacement of human labor. Automation robots have found applications in the defense sector, the intelligence community, the medical field, and other manufacturing sectors. These very advanced electromechanical bots are AI machines with extensive computer programming. This research analyzes the implementation of a robotic surveillance system designed for high-risk, out-of-the-way places like border monitoring or war zones. This technology's major goal is to replace troops in border monitoring responsibilities. Many defense departments use robots to do duties that jeopardize human lives. Cameras, internet access, live video streaming, GSM modems, wireless communication modules, and life-saving sensors are among the integrated technologies used in these defensive robots. The transmitter sends a control signal to the receiver when a target vehicle or item is linked and intended for remote control. Military robots may be remotely monitored and commanded using internet of Things (IoT) technology. The various defense robots have optimized configurations for their roles. A multipurpose defense robot driven by LoRa technology lowers human error in defensive operations and saves lives. This complex infrastructure protects lives and the nation from all dangers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multipurpose robots

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DSP Implementation of Contrast Enhancement by Nonlinear Diffusion 23

DSP Implementation of Contrast Enhancement by Nonlinear Diff...

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15th international conference on Digital image Processing, ICDIP 2023

作者： Liu, Weijian Liang, Zhetong Yao, Ruohe Huang, Qiaojie School of Communication Engineering Guangzhou City University of Technology China School of Electronic and Information Engineering South China University of Technology China Guangdong Oppo Moblie Telecommucations Corp. Ltd China School of Intelligent Engineering Guangdong Agriculture Industry Business Polytechnic China

ISBN: (纸本)9798400708237

A real-time image enhancement algorithm is usually needed by an image processing system to improve the visual quality of the captured image sequence. Contrast enhancement by nonlinear diffusion (CEND) in the literature shows excellent performance in artifact removal and local contrast enhancement. However, CEND algorithm is too complex to be implemented on real-time systems. In this paper, in order to achieve real-time image enhancement algorithm based on CEND, we choose C674x DSP (Digital signal Processing) as targeted device and perform code simplification as well as in-depth code optimization, including compiler-based optimization, calculation simplification and on-chip memory utilization. These optimization measures do not involve access to peripherals, which makes the algorithm stable and convenient to be transplanted to other DSP platforms. We test the program of the CEND algorithm on TMS320DM8168 evaluation board and have verified that the implemented algorithm as a single thread on DSP subsystem is able to achieve above 25fps for 640×480 sized image sequence. © 2023 ACM.

关键词： image enhancement

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A Denoising Method for Motor Vibration signal based on VMD and Wavelet Threshold 4

A Denoising Method for Motor Vibration Signal Based on VMD a...

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4th international conference on Information Science, Parallel and Distributed systems, ISPDS 2023

作者： Fang, Ting Yang, Chunsheng Hu, Chenghe Dong, Chong Han, Jiaming Luo, Shijian School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China

ISBN: (纸本)9798350337181

Industrial production relies heavily on electric motors, but the motor vibration signals collected are often noisy, making it difficult to analyze the motor's performance. As a noise reduction method, the wavelet threshold has been used. However, the conventional methods of processing signals using soft and hard thresholds have issues such as the pseudo-Gibbs phenomenon and deviation in signal reconstruction. This paper proposes a solution to tackle the problems by introducing an algorithm that combines the variational mode decomposition (VMD) with a modified wavelet threshold noise reduction method. The improved sparrow search algorithm (SSA) is first applied to determine the optimal values of the number of modal components K and the penalty factor a. Then VMD decomposes the acquired motor vibration signal to obtain K intrinsic mode functions (IMF). Finally, the IMFs containing noise is processed by an improved wavelet threshold noise reduction method, which is used to obtain the noise-reduced signal after reconstruction. The effectiveness and superiority of the method proposed in this paper is verified by comparing with other methods in the experiments. The results show that the proposed noise reduction method effectively reduces noise interference in the motor vibration signal and is practical for use in industrial applications. © 2023 IEEE.

关键词： Variational mode decomposition

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