  Background: Regarding the importance of right diagnosis in medical
applications, various methods have been exploited for processing medical images
solar. The method of segmentation is used to analyze anal to miscall structures
in medical imaging. Objective: This study describes a new method for brain
Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on
genetic and regional growth. Methods: Among medical imaging methods, brains MRI
segmentation is important due to the high contrast of non-intrusive soft tissue
and high spatial resolution. Size variations of brain tissues are often
accompanied by various diseases such as Alzheimers disease. As our knowledge
about the relationship between various brain diseases and deviation of brain
anatomy increases, MRI segmentation is exploited as the first step in early
diagnosis. In this paper, the regional growth method and auto-mate selection of
initial points by genetic algorithm are used to introduce a new method for MRI
segmentation. Primary pixels and similarity criterion are automatically by
genetic algorithms to maximize the accuracy and validity in image segmentation.
Results: By using genetic algorithms and defining the fixed function of image
segmentation, the initial points for the algorithm were found. The proposed
algorithms are applied to the images and results are manually selected by
regional growth in which the initial points were compared. The results showed
that the proposed algorithm could reduce segmentation error effectively.
Conclusion: The study concluded that the proposed algorithm could reduce
segmentation error effectively and help us to diagnose brain diseases.
