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Hybrid Segmentation Approach for Different Medical Image Modalities

Computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid el-Shafai Amira A.Mahmoud el-Sayed M.el-Rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi e.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar properties.each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)algorithms.An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques.The existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed algorithms.This paper extensively analyzes and summarizes the comparative investigation of these techniques.Finally,a prediction of the improvement involves the combination of these techniques is suggested.The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PeT images MR images fuzzy c-mean morphological operations active contour

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efficient Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid el-Shafai Amira A.Mahmoud el-Sayed M.el-Rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi e.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations are employed to ensure image detail protection and noise-immunity.The objective of using morphological operations is to remove the defects in the texture of the image.Secondly,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers.The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers.It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix.Simulation results are performed on different medical image modalities.Ultrasonic(Us),X-ray(Mammogram),Computed Tomography(CT),Positron emission Tomography(PeT),and Magnetic Resonance(MR)images are the main medical image modalities used in this work.The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation.Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PeT,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PeT MRI morphological operations FCM active contours

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efficient Deep CNN Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid el-Shafai Amira A.Mahmoud el-Sayed M.el-Rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Mohammed Abd-elnaby Fathi e.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Deep CNN Model for Multimodal Medical Image Denoising

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Computers, Materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid el-Shafai Amira A.Mahmoud Anas M.Ali el-Sayed M.el-Rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi e.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of engineering&Technology(AIeT) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron emission Tomography(PeT).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)techniques.In the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI techniques.SI denoising techniques can be carried out both in a transform or spatial domain.This paper is concerned with single-image noise reduction techniques because we deal with single medical images.The most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are studied.Also,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are investigated.Finally,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities.This model utilizes the Batch Normalization(BN)and the ReLU as a basic structure.As a result,it attained a considerable improvement over the traditional techniques.The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied universally.Based on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

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智能电网智能逆变器的开发与设计

智能电网智能逆变器的开发与设计

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作者： taha Ather 华北电力大学(北京)

学位级别：硕士

人们对太阳能消除和防止气候变化的巨大兴趣、光伏系统总发电率的下降以及许多有利的情况使光伏系统的市场利用率最大化,并增加了全球能源供应。分布式发电系统的主要部件是一个以并网方式连接的智能逆变器,它直接连接电网和可再生能源... 详细信息

人们对太阳能消除和防止气候变化的巨大兴趣、光伏系统总发电率的下降以及许多有利的情况使光伏系统的市场利用率最大化,并增加了全球能源供应。分布式发电系统的主要部件是一个以并网方式连接的智能逆变器,它直接连接电网和可再生能源。本文致力于在分布式发电和可再生能源的有功、无功调节三相系统中开发并网逆变器。由于逆变器具有各种优点,所以要设计和仿真的是电压源逆变器。本文提出应用规划滤波器将电网中引入的谐波数量按规定降到可接受的水平,同时引入逆变器功率输出的实际功率和无功分量的控制方法,以帮助解决现有系统的不稳定问题和有关电能质量的关注。本文旨在讨论系统的设计步骤、性能等几个特点。在MATLAB/Simulink平台上进行仿真验证,为论文工作提供依据。我们的目标是结合V.S.I.设计L.C.L.,以同步参考系原理为基础的电流控制方案,并使用PI控制器,以解决电能质量问题,并在电流注入电网前抑制谐波。所设计的系统旨在对功率水平的变化作出快速反应,而不会对现有系统产生任何重大影响,也不会断开连接,从而满足在并网操作模式下完成给定任务的要求。

关键词： ReS1 电流控制器2 电压源逆变器3 固定参考系4 G.C.I 5 T.H.D 6 电能质量7

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Anchor Selection for Topology Inference and Routing in Wireless Sensor Networks

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Journal of Communications and Information Networks 2020年第3期5卷 318-323页

作者： taha Bouchoucha Zhi Ding University of California DavisCalifornia 95616USA

Anchor-based ad-hoc networks utilize hop measurements to generate a virtual coordinate system for topology inference and routing applications.A common problem with such coordinate system is its sensitivity to anchor placement.We present a general formulation to the anchor node selection problem.Then,we relax the optimization problem by deriving an upper-bound of the objective function.We finally propose an iterative algorithm that consists in choosing additional anchor nodes based on the connectivity information provided by the current anchor set.Numerical simulations indicate that our anchor selection method is robust to missing measurements and improves network topology inference and routing performance.

关键词： anchor selection connectivity hop distance routing

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Variational approach for the flow of Ree–eyring and Casson fluids in pipes

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International Journal of Modeling, Simulation, and Scientific Computing 2016年第2期7卷 126-138页

作者： taha Sochi University College London Department of Physics&Astronomy Gower Street LondonWC1E 6BTUK

The flow of Ree–eyring and Casson non-Newtonian fluids is investigated using a variational principle to optimize the total stress.The variationally obtained solutions are compared to the analytical solutions derived from the Weissenberg–Rabinowitsch–Mooney equation and the results are found to be identical within acceptable numerical errors and modeling approximations.

关键词： Fluid mechanics tube flow Ree-eyring Casson Weissenberg-Rabinowitsch-Mooney variational method total stress optimization.

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Biomarker bust:meta-analyses reveal unreliability of neuronal extracellular vesicles for diagnosing parkinsonian disorders

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Neural Regeneration Research 2025年第1期20卷 201-202页

作者： Hash Brown taha Leonard Davis School of Gerontology University of Southern CaliforniaLos AngelesCAUSA

A range of neurodegenerative disorders,collectively termed parkinsonian disorders,present with a complex array of both motor and non-motor symptoms.Included in this group are Parkinson’s disease(PD),dementia with Lewy bodies(DLB),multiple system atrophy(MSA),corticobasal syndrome(CBS),and progressive supranuclear palsy(PSP).These disorders are differentiated neuropathologically by their dominant protein pathologies involvingα-synuclein(α-syn)and/or tau,the types of brain cells affected,such as neurons,oligodendroglia,and astrocytes,and the specific brain regions involved(Tolosa et al.,2021).

关键词： disorders parkin

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Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

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Plant Phenomics 2023年第2期5卷 139-148页

作者： Lei Zhou Qinlin Xiao Mohanmed Farag taha Chengjia Xu Chu Zhang College of Mechanical and electronic engineering Nanjing Forestry UniversityNanjingChina College of Biosystems engineering and Food Science Zhejiang UniversityZhejiangChina Department of Soil and Water Sciences Faculty of Environmental Agricultural SciencesArish UniversityNorth Sinai45516Egypt School of Information engineering Huzhou UniversityHuzhouChina

Deep learning and computer vision have become emerging tools for diseased plant phenotyping.Most previous studies focused on image-level disease classification.In this paper,pixel-level phenotypic feature(the distribution of spot)was analyzed by deep learning.Primarily,a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed.A dataset of apple leaves samples was used for training and optimization.Another set of grape and strawberry leaf samples was used as an extra testing dataset.Then,supervised convolutional neural networks were adopted for semantic segmentation.Moreover,the possibility of weakly supervised models for disease spot segmentation was also explored.Grad-CAM combined with ResNet-50(ResNet-CAM),and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation(WSLSS),was designed.They were trained using image-level annotations(healthy versus diseased)to reduce the cost of annotation work.Results showed that the supervised DeepLab achieved the best performance(IoU=0.829)on the apple leaf dataset.The weakly supervised WSLSS achieved an IoU of 0.434.When processing the extra testing dataset,WSLSS realized the best IoU of 0.511,which was even higher than fully supervised DeepLab(IoU=0.458).Although there was a certain gap in IoU between the supervised models and weakly supervised ones,WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure.Furthermore,the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.

关键词： Deep computer weakly

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Multi-period Power System State estimation with PMUs Under GPS Spoofing Attacks

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Journal of Modern Power Systems and Clean energy 2020年第4期8卷 597-606页

作者： Paresh Risbud Nikolaos Gatsis Ahmad taha Department of electrical and Computer engineering University of Texas at San AntonioSan AntonioUSA Ieee

This paper introduces a dynamic network model together with a phasor measurement unit(PMU)measurement model suitable for power system state estimation under spoofing attacks on the global positioning system(GPS)receivers of PMUs.The spoofing attacks may introduce time-varying phase offsets in the affected PMU measurements.An algorithm is developed to jointly estimate the state of the network,which amounts to the nodal voltages in rectangular coordinates,as well as the time-varying attacks.The algorithm features closedform updates.The effectiveness of the algorithm is verified on the standard Ieee transmission networks.It is numerically shown that the estimation performance is improved when the dynamic network model is accounted for compared with a previously reported static approach.

关键词： Dynamic state equation global positioning system(GPS)spoofing phasor measurement unit(PMU) state estimation time synchronization weighted least squares

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