Computer Science and Information Systems 2013 Volume 10, Issue 3, Pages: 1319-1342
https://doi.org/10.2298/CSIS120604050Z
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A novel approach based on genetic algorithms and region growing for magnetic resonance image (MRI) segmentation
Zanaty Elnomery A. (College of Computers and IT, Taif University, Taif, Saudi Arabia + Department of Mathematics, Faculty of Science, Sohag University, Egypt)
Ghiduk Ahmed S. (College of Computers and IT, Taif University, Taif, Saudi Arabia + Department of Mathematics, Faculty of Science, Beni-Suef University, Egypt)
This paper presents a new segmentation approach based on hybridization of the
genetic algorithms (GAs) and seed region growing to produce accurate medical
image segmentation, and to overcome the oversegmentation problem. A new
fitness function is presented for generating global minima of the objective
function, and a chromosome representation suitable for the process of
segmentation is proposed. The proposed approach starts by selecting a set of
data randomly distributed all over the image as initial population. Each
chromosome contains three parts: control genes, graylevels genes, and
position genes. Each gene associates the intensity values by their positions.
The region growing algorithm uses these values as an initial seeds to find
accurate regions for each control gene. The proposed fitness function is used
to evolve the population to find the best region for each control gene.
Chromosomes are updated by applying the operators of GAs to evolve
segmentation results. Applying the proposed approach to real MRI datasets,
better results were achieved compared with the clustering-based fuzzy method.
Keywords: image segmentation, genetic algorithms, region growing method, fuzzy c-means