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FPGA implementation of particle swarm optimization based on new fitness function for MRI images segmentation
Author(s) -
Hamdaoui Fayçal,
Sakly Anis,
Mtibaa Abdellatif
Publication year - 2015
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22130
Subject(s) - field programmable gate array , computer science , particle swarm optimization , segmentation , fitness function , image segmentation , function (biology) , computer vision , generator (circuit theory) , artificial intelligence , computer hardware , algorithm , genetic algorithm , machine learning , physics , evolutionary biology , biology , power (physics) , quantum mechanics
Magnetic resonance imaging (MRI) is considered as a key part in therapeutic procedures because it clearly defines the aim. It also avoids sensitive organs and it determines the desired paths. This phenomenon requires image processing operations such as segmentation to locate the tumor. Medical image segmentation is still an important topic in the field of brain tumor. In the present article, we propose a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization (HAMPSO) algorithm for MRI images segmentation. To achieve this, we use the Xilinx System Generator (XSG) to be implemented on a Field Programmable Gate Array (FPGA). This architecture is based on a new variant of objective function. These performances of the proposed method are proved using a set of MRI images and were compared to the Hardware Architecture of segmentation based on Particle Swarm Optimization (HAPSO) in terms of either device utilization, execution time, qualitatively or quantitatively results.