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Medical Image Compression Based on Wavelets with Particle Swarm Optimization

Computers, Materials & Continua 2021年第5期67卷 1577-1593页

作者： Monagi H.Alkinani e.a.zanaty Sherif M.Ibrahim Department of Computer Science and Artificial Intelligence Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabi Department of Computer Science Faculty of Computers and InformationSohag UniversitySohagEgypt Department of Computer Science and Mathematics Faculty of ScienceSouth Valley UniversityQenaEgypt

This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image *** method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using *** transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a *** is applied for selecting a particle assigned to the threshold values for the *** positions assigned to particles values are used to represent *** particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe *** experiments are applied on five different medical image types,i.e.,MRI,CT,and *** show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.

关键词： Image compression wavelets Haar wavelet particle swarm algorithm medical image compression PSNR and CR

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Fusion Strategy for Improving Medical Image Segmentation

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Computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady e.a.zanaty Aida HAbu bakr Walaa M.Abd-elhafiez Department of Computer engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation

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Open Journal of Medical Imaging 2013年第4期3卷 125-135页

作者： elnomery Allam zanaty Mathematics Department Faculty of Science Sohag University Sohag Egypt

In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (FCM) and possiblistic c-means (PCM) functions. It is realized by modifying the objective function of the conventional PCM algorithm with Gaussian exponent weights to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. For that, the proposed algorithm is capable to avoid various problems of existing fuzzy clustering methods that solve the defect of noise sensitivity and overcomes the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments by applying them to the challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets and by comparison with other state of the art algorithms. The experimental results show that the proposed method produces accurate and stable results.

关键词： Fuzzy Clustering Possiblistic C-Means Medical Image Segmentation

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