萤火虫群优化算法多阈值的脑CT图像分割

Brain CT segmentation based on multi-threshold optimized by glowworm swarm optimization algorithm

在基于多阈值的脑,CT图像分割算法中,最佳阈值选取是脑CT图像中的关键,针对传统多阈值法的阈值选择难题为了提高脑。CT图像的分割准确率,提出一种萤火虫群算法优化多阈值的脑CT图像分割方法首先建立了基于多阈值法的脑图像分割数学模型,然后通过萤火虫群算法数学模型进行求解,搜索到脑CT图像分割的最佳阈值,CT最后采用最佳阈值完成脑CT图像的分割。仿真结果表明,萤火虫群算法提高了脑CT图像的精度,获得了更加理想的脑CT图像结果。

The optimal threshold is the key in the brain tumor image segmentation of image threshold segmentation algorithm. In order to improve the segmentation accuracy of brain tumor, a novel brain tumor image segmentation method based on multi-threshold optimized by glowworm swarm optimization algorithm was proposed in this paper. Firstly, the mathematic model of multi-threshold method was established, secondly, glowworm swarm optimization al_gorithm was used to solve the mathematic model and find the optimal segmentation threshold of the image, and finally, image was segmented according to the optimal threshold. The results showed that our algorithm has improved brain tumor image segmentation accuracy and obtained better results of brain tumor image segmentation.