中文摘要：

为增强水平集主动轮廓算法的噪声鲁棒性,考虑医学图像的灰度复杂、拓扑结构多变等情况,发挥水平集主动轮廓模型不依赖于初始轮廓的特性,提出一种基于聚类与局部统计的水平集医学图像分割方法。对传统的K均值与RSF模型进行改进,提高噪声的鲁棒性,能有效地处理真实医学图像的灰度非均匀性。进行CT和MR医学实验验证,实验结果表明,该方法能够比传统方法更有效地应对噪声干扰,提高了医学图像的分割精度和效果。

英文摘要：

To enhance the noise robustness of level set active contour algorithm,considering the medical image gray-scale complex,changing topological structure,and so on and so forth,using the feature that level set active contour model is not dependent on the characteristics of the initial contour,level set segmentation based on clustering and local statistics for medical image was proposed.The methods of traditional K-means and RSF model were improved,effectively improving the noise robustness,and the intensity inhomogeneity was effectively dealt with,and CT and MR medical experimental verification was carried out.Experimental results show that the method is more effective than the traditional method when dealing with noise,the precision and effects of medical image segmentation were improved.