Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth.
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 high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer's disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is 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.


CONCLUSIONS
The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.
Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Javadpour
J
A
Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mohammadi
M
A
Brain Magnetic Resonance Image; Genetic Algorithm; Regional Growth; Segmentation
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