基于K均值聚类的图割医学图像分割算法

Graph Cuts Medical Image Segmentation Algorithm Based on K-means Clustering

图割是一种同时基于区域和边界的交互式图像分割算法。传统的基于高斯混合模型的图割具有时间慢和描述组织中灰度分布不准确的缺点。为此,提出一种基于K均值聚类的图割算法。通过用改进的图割来分割仿体的和真实的脑部核磁共振图像,显示出该方法的有效性。该方法不但能提高图割在分割时的速度,在有噪音和灰度不均匀的图像上也能在较短的时间内得到更准确且鲁棒的结果。

Graph cuts is an interactive segmentation algorithm based on boundary and region properties of objects in images.The region term in conventional graph cuts is based on Gaussian Mixture Model（GMM）.However,it is not only a slow process,but sometimes it can＇t describe the distribution of pixels in objects precisely.This paper proposes an improved algorithm based on K-means clustering graph cuts.Its evaluation is performed using both phantoms and real Magnetic Resonance Imaging（MRI） of brain,the effectiveness and efficiency of the proposed algorithm are showed.And in particular,an accurate and robust results in segmenting images with noise and intensity non-uniformity with a low computational cost can be achieved.