Comparison between MPSO and MSFLA metaheuristics for MR brain image segmentation

Author

Hamdaoui, F. ; Mtibaa, A. ; Sakly, A.

Author_Institution

Lab. of EμE, Univ. of Monastir, Monastir, Tunisia

fYear

2014

Firstpage

164

Lastpage

168

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.

Keywords

biomedical MRI; brain; image segmentation; particle swarm optimisation; MPSO; MR brain image segmentation; MR brain medical images; MSFLA metaheuristics; binary image; image threshold values; metaheuristics swarm intelligence techniques; modified PSO algorithm; modified SFLA algorithm; particle swarm optimization; shuffled frog leaping algorithm; Algorithm design and analysis; Brain; Image segmentation; Particle swarm optimization; Partitioning algorithms; Sociology; Statistics; Comparison; MPSO; MR brain images; MSFLA; New Fitness Function; Segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2014 15th International Conference on

Type

conf

DOI

10.1109/STA.2014.7086725

Filename

7086725