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Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm
Zhang Yang1, Ye Shufan2, Guo Li3
1School of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
Biomed Research International
|July 13, 2016
Summary
This study enhances the harmony searching (HS) algorithm using rough set theory for improved optimization. The modified HS algorithm achieves better convergence and accuracy, outperforming the original method in magnetic resonance imaging (MRI) brain image segmentation.
Area of Science:
- Computational intelligence
- Optimization algorithms
- Medical image analysis
Background:
- The harmony searching (HS) algorithm is a metaheuristic optimization technique used in various applications.
- Improving the convergence speed and accuracy of HS is crucial for complex problems.
- Magnetic resonance imaging (MRI) brain image segmentation benefits from accurate optimization methods.
Purpose of the Study:
- To propose a modified harmony searching (HS) algorithm for enhanced efficiency and accuracy.
- To integrate rough set theory for improved HS algorithm performance.
- To apply the improved HS algorithm for magnetic resonance imaging (MRI) brain image segmentation.
Main Methods:
- A modified harmony searching (HS) algorithm incorporating rough set theory was developed.
- The improved HS algorithm was used to find optimal convergence values.
- These optimal values served as initial parameters for a fuzzy clustering algorithm in MRI brain image segmentation.
Main Results:
- The modified HS algorithm demonstrated superior convergence and accuracy compared to the original HS algorithm.
- The improved algorithm achieved more precise results in optimization tasks.
- MRI brain image segmentation using the enhanced algorithm yielded better results than the standard fuzzy clustering method.
Conclusions:
- The integration of rough set theory significantly enhances the harmony searching algorithm's efficiency and accuracy.
- The improved HS algorithm provides a robust optimization tool for complex problems.
- This enhanced approach offers superior performance for magnetic resonance imaging (MRI) brain image segmentation.
