A Novel Approach Based on Genetic Algorithms and Region Growing for Magnetic Resonance Image (MRI) Segmentation

Elnomery A. Zanaty1, 2 and Ahmed S. Ghiduk1, 3

  1. College of Computers and IT, Taif University
    Taif, Saudi Arabia
    {n.allam, asaghiduk}@ tu.edu.sa
  2. Department of Mathematics, Faculty of Science
    Sohag University, Egypt
  3. 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, gray-levels 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.

Key words

Image segmentation, genetic algorithms, region growing method, fuzzy c-means.

Digital Object Identifier (DOI)


Publication information

Volume 10, Issue 3 (June 2013)
Year of Publication: 2013
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Zanaty, E. A., Ghiduk, A. S.: A Novel Approach Based on Genetic Algorithms and Region Growing for Magnetic Resonance Image (MRI) Segmentation. Computer Science and Information Systems, Vol. 10, No. 3, 1319-1342. (2013)