{"title":"MPSO与MSFLA元启发式脑磁共振图像分割的比较","authors":"F. Hamdaoui, A. Mtibaa, A. Sakly","doi":"10.1109/STA.2014.7086725","DOIUrl":null,"url":null,"abstract":"This paper presents a comparison study between two metaheuristics swarm intelligence (SI) techniques based Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA), to solve images segmentation problems. Performances in terms of Threshold values and run time execution of both Modified PSO (MPSO) and Modified SFLA (MSFLA) algorithms are reviewed and checked through MR brain medical images application that consist of partitioning an image into two regions, so get a binary image. MPSO and MSFLA are based on a new fitness function, which justifies their appointment.","PeriodicalId":125957,"journal":{"name":"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison between MPSO and MSFLA metaheuristics for MR brain image segmentation\",\"authors\":\"F. Hamdaoui, A. Mtibaa, A. Sakly\",\"doi\":\"10.1109/STA.2014.7086725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a comparison study between two metaheuristics swarm intelligence (SI) techniques based Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA), to solve images segmentation problems. Performances in terms of Threshold values and run time execution of both Modified PSO (MPSO) and Modified SFLA (MSFLA) algorithms are reviewed and checked through MR brain medical images application that consist of partitioning an image into two regions, so get a binary image. MPSO and MSFLA are based on a new fitness function, which justifies their appointment.\",\"PeriodicalId\":125957,\"journal\":{\"name\":\"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STA.2014.7086725\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA.2014.7086725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison between MPSO and MSFLA metaheuristics for MR brain image segmentation
This paper presents a comparison study between two metaheuristics swarm intelligence (SI) techniques based Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA), to solve images segmentation problems. Performances in terms of Threshold values and run time execution of both Modified PSO (MPSO) and Modified SFLA (MSFLA) algorithms are reviewed and checked through MR brain medical images application that consist of partitioning an image into two regions, so get a binary image. MPSO and MSFLA are based on a new fitness function, which justifies their appointment.
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