改进布鸟搜索算法最大熵值的医学图像分割

Medical image segmentation based on Maximum Entropy Multi-Threshold Segmentation by Improved Cuckoo Search Algorithm

研究医学图像分割问题。医学图像是医学影像的分析基础,医学图像由于组织边缘模糊和灰度不均匀含噪声等特点,导致最大熵值分割医学图像算法难以进行准确分割,分割精度低,为了提高医学图像分割的准确性,提出一种改进布鸟搜索算法优化最大熵值的医学图像分割方法。首先由最大熵法找到医学图像分割目标函数,然后采用改进布谷鸟搜索算法对目标函数进行优化,找到医学图像的最佳分割点,实现医学图像分割,最后采用多幅医学图像进行仿真,以测试算法性能。结果表明,改进方法不仅解决了传统最大熵值医学图像分割算法存在的缺陷,同时提高医学图像分割的精度,并且具有较好的鲁棒性,具有较好的实际应用价值。

In order to improve the effect of medical image segmentation, this paper proposes a novel medical im- age segmentation method based on maximum entropy multi - threshold segmentation optimized by improved cuckoo search algorithm. Firstly, threshold optimization objective function of maximum entropy method is obtained, and then improved cuckoo search algorithm is used to solve the objective function and find the optimal segmentation threshold of the medical image. Finally, medical image is segmented according to the optimal threshold, and the performance is tested by simulation. The results show that the proposed method can improve the accuracy of medical image seg- mentation and has good robustness.