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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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Resveratrol Modulates Bone Mineral Density and Bone Mineral Content in A Rat Model of Male Hypogonadism

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Chinese Journal of Integrative Medicine 2023年第2期29卷 146-154页

作者： Hussein F.Sakr Boudaka Ammar amira AlKharusi I Al-Lawati mahmoud AlKhateeb Basim H Elesawy Department of Physiology College of Medicine and Health SciencesSultan Qaboos UniversityMuscat 123Oman Medical Physiology Department Faculty of MedicineMansouraUniversityMansoura 35516Egypt Basic Medical Sciences Department College of Medicine at King Saud bin Abdulaziz University for Health Sciences(KSAU-HS)Riyadh 11481Saudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityTaif 21944Saudi Arabia

Objective: To determine whether resveratrol(Res) can correct osteoporosis induced in a rat model of male hypogonadism. Methods: Thirty-two rats were randomly divided into 4 groups, 8 in each group;1) a control sham group: underwent a similar surgical procedure for induction of orchiectomy(ORCD) without ligation of any arteries or veins or removal of the testis and epididymis;2) a control + Res-treated group(Con+Res):underwent sham surgery similar to the control, but was then treated with Res, as described below;3) an ORCDinduced group: bilateral ORCD surgery as described above, and 4) a ORCD+Res-treated group: bilateral ORCD surgery followed by Res treatment. Res treatment began 4 weeks after ORCD and continued for 12 weeks. After 12 weeks, bone mineral density(BMD) and bone mineral content(BMC) were measured in the tibia and femur of each rat’s right hind leg. Blood levels of bone turnover indicators such as deoxypyridinoline(Dpd), N-telopeptide of type Ⅰ collagen(NTXⅠ), alkaline phosphatase(ALP), and osteocalcin(OC), as well as receptor activator of nuclear factor kappa B(RANK) and osteoprotegerin(OPG) were assessed. Results: ORCD significantly decreased BMD(P<0.01) and significantly increased bone resorption, manifested by increased RANK. In addition, it inhibited serum levels of OPG and OC. Res treatment after ORCD effectively increased serum levels of bone formation markers such as OPG and OC, compared with testisectomized rats(P<0.05). Conclusion:Res could ameliorate bone loss induced by male hypogonadism, possible via restoration of the normal balance between RANK and OPG.

关键词： osteoporosis male hypogonadism resveratrol receptor activator of nuclear factor-kappa

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Axonal degeneration of the ulnar nerve secondary to carpal tunnel syndrome: fact or fiction?

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Neural Regeneration Research 2013年第15期8卷 1418-1422页

作者： Radwa.mahmoud Azmy amira Ahmed Labib Saly Hassan Elkholy Department of Special Medicine Faculty of Medicine Cairo University

The distribution of sensory symptoms in carpal tunnel syndrome is strongly dependent on the degree of electrophysiological dysfunction of the median nerve. The association between carpal tunnel syndrome and ulnar nerve entrapment is still unclear. In this study, we measured ulnar nerve function in 82 patients with carpal tunnel syndrome. The patients were divided into group I with minimal carpal tunnel syndrome （n = 35） and group II with mild to moderate carpal tunnel syndrome （n = 47） according to electrophysiological data. Sixty-one age- and sex-matched subjects without carpal tunnel syndrome were used as a control group. There were no significant differences in ulnar sensory nerve peak latencies or conduction velocities from the 4th and 5th fingers between patients with carpal tunnel syndrome and the control group. The ulnar sensory nerve action potential amplitudes from the 4th and 5th fingers were lower in patients with carpal tunnel syndrome than in the control group. The ratios of the ulnar sensory nerve action potential amplitudes from the 4th and 5th fingers were almost the same in patients with carpal tunnel syndrome as in the control group. These findings indicate that in patients with minimal to moderate carpal tunnel syndrome, there is some electrophysiological evidence of traction on the adjacent ulnar nerve fibers. The findings do not indicate axonal degeneration of the ulnar nerve.

关键词： neural regeneration peripheral nerve injury carpal tunnel syndrome median nerve extra-mediansymptoms motor conduction sensory conduction ulnar nerve ulnar amplitude ratio neuroregeneration

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Recent advances in thin film composites membranes for brackish groundwater treatment with critical focus on Saskatchewan water sources

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Journal of Environmental Sciences 2019年第7期31卷 181-194页

作者： Arash Mollahosseini amira Abdelrasoul Department of Chemical and Biological Engineering University of Saskatchewan Global Institute of Water Security

Drinking water scarcity is an ever-increasing global concern. This issue appears as a greater threat to the countries with no access to sea water resources or rivers, since their potential water resources are only limited to ground waters only. There are serious concerns with the treatment of ground water resources, including landfill leachates, agricultural contaminations(pesticides,herbicides, and fertilizers), and rural contaminations. Membrane separation has been proved to be the governing technology in water and wastewater treatment plants, as these methods are responsible for more than half of the market share of the world's desalination capacity. This study intends to offer a holistic view of the groundwater contamination with specific focus on Saskatchewan province in Canada, and the recent efforts in the groundwater treatment using thin film composite membrane technology. This study begins with an introduction of the general aspects of ground water and membrane separation, polluting agents, and their sources.It is followed by a discussion of Saskatchewan's groundwater status and various issues.Furthermore, the recent research that became available since 2010 is reviewed in details and the results are summarized with respect to purification efficiency. Different affecting parameters in a groundwater-thin film composite system are synthesized and an in-depth overview is presented.

关键词： Thin film composite membrane Groundwater Brackish water Water pollution Polyamide Canada Saskatchewan

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A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

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Computers, Materials & Continua 2022年第1期70卷 1381-1399页

作者： Ahmed Reda Sherif Barakat amira Rezk Department of Information System Faculty of Computer and Information ScienceMansoura UniversityMansouraEgypt

Many respiratory infections around the world have been caused by coronaviruses.COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate.There is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems.Machine learning(ML)has been used to examine chest X-ray frames.In this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal person.First,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature extraction.Second,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN models.These selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray images.The proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

关键词： Butterfly optimization algorithm(BOA) covid-19 chest X-ray images convolutional neural network(CNN) extreme learning machine(ELM) feature selection

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Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 26-39页

作者： Imene Mecheter Maysam Abbod Habib Zaidi Abbes amira Brunel University LondonUK Geneva University Hospital GenevaSwitzerland Geneva University GenevaSwitzerland University of Groningen GroningenNetherlands University of Southern Denmark OdenseDenmark University of Sharjah SharjahUAE De Montfort University LeicesterUK

Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.

关键词： computer vision convolution image segmentation learning(artificial intelligence)

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An Efficient Ensemble Model for Various Scale Medical Data

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Computers, Materials & Continua 2022年第10期73卷 1283-1305页

作者： Heba A.Elzeheiry Sherief Barakat amira Rezk Information System Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.

关键词： Electronic health records(EHRs) Random forest(RF) Decision tree(DT) linear model(LR) Multi-layer Perceptron(MLP) MDRL correlation feature selection(CFS)

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