Title :
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
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;
Conference_Titel :
Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2014 15th International Conference on
DOI :
10.1109/STA.2014.7086725
