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

TL;DR

This paper introduces a new MRI brain segmentation method combining genetic algorithms and regional growth to improve accuracy and reduce errors, aiding early diagnosis of brain diseases.

Contribution

A novel algorithm integrating genetic algorithms with regional growth for brain MRI segmentation, automating initial point selection and enhancing segmentation accuracy.

Findings

Reduced segmentation errors compared to traditional methods

Automated initial point selection improves accuracy

Effective in diagnosing brain diseases

Abstract

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.