概率加权测地距离的脑部MR图像超像素分割

超像素是一种重要的图像过分割,因为医学图像具有边界模糊、不同组织的灰度范围互相重叠的特点,为超像素分割带来极大困难.针对脑部MR图像超像素生成问题,从脑部MR图像的特点出发,充分利用脑部MR图像表达先验知识,结合脑部MR图像的一般结构,定义每个像素属于脑组织中一个类别的概率,并基于分类概率提出一种有效的边界梯度计算方法;在此基础上,提出一种概率密度加权的测地距离脑部MR图像超像素分割算法;最后应用模糊C均值聚类算法作为后续分割处理,获得脑部MR图像的组织分类.与现有算法在分割性能上进行定量比较的实验结果表明,文中算法能够产生更准确的分割边界.

Superpixel Segmentation of Brain MR Images Based on Probabilistic Weighted Geodesic Distance

While superpixel segmentation is a significant over-segmentation technique, it is extremely difficult to perform it on medical image for blurred boundaries and overlapping grayscales of different tissues. This paper bases on the characteristic of brain MR images, aims to deal with the problem of brain MR images superpixels generation. We make full use of the prior of brain MR images, combine the structure of the brain MR images and define a probability every pixel belongs to each class. An efficient method to compute boundary gradient based on classification probability is proposed. Based on the above, we propose a probability density weighted geodesic distance for superpixel segmentation. The fuzzy C-means method is applied as subsequent processing to obtain the classification of brain image. By comparing segmentation performance with the state-of-the-art methods in quantitative analysis, our algorithm generates more accurate segmentation boundaries.