基于人工蜂群优化的MR图像分割算法研究

针对传统图像阈值分割算法在MR图像分割时存在的易受采集图像灰度不均、医学图像易受噪声干扰,因而难以得到准确分割阈值的问题,本文将人工蜂群算法与二维OSTU阈值分割算法相结合,提出一种基于人工蜂群优化的MR图像分割算法。使用医学图像的离散度矩阵的迹作为人工蜂群优化的目标函数,得到二维OSTU的最佳分割阈值;根据得到的最佳阈值,对图像采用二维OSTU分割的方法进行分割。实验结果证明,对于医学MR图像,本文所提出的算法具有精度高和鲁棒性强的特点,能够得到精确的分割后图像。

Research on MR Image Segmentation Algorithm Based on Artificial Bee Colony Optimization

Traditional image threshold segmentation algorithm in MR image segmentation is easy to be disturbed by the problem of gray un-uniform and being susceptible to noise interference.Considering these problems,an MR image segmentation algorithm based on artificial bee colony optimization is proposed,which is combining the artificial bee colony algorithm with the two-dimensional OSTU threshold segmentation algorithm.The algorithm uses the trace of the dispersion matrix of medical image as the objective function of artificial bee colony optimization,the optimal segmentation threshold of two-dimensional OSTU is obtained.According to the optimal threshold,the image is segmented by two-dimensional OSTU.The experimental results show that,for medical MR images,the algorithm proposed in this paper has the characteristics of high accuracy and strong robustness,and can get accurate segmented images.