Medical Image Compression Based on Wavelets with Particle Swarm Optimization

作     者：Monagi H.Alkinani E.A.Zanaty Sherif M.Ibrahim 

作者机构：Department of Computer Science and Artificial IntelligenceFaculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabi Department of Computer ScienceFaculty of Computers and InformationSohag UniversitySohagEgypt Department of Computer Science and MathematicsFaculty of ScienceSouth Valley UniversityQenaEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第5期

页      面：1577-1593页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：funded by the University of Jeddah Saudi Arabia under Grant No.UJ-20-043-DR 

主　　题：Image compression wavelets Haar wavelet particle swarm algorithm medical image compression PSNR and CR 

摘      要：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.