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

Improving Brain Magnetic Resonance Image MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth