分割是在MRI analysis.We的基本问题之一，同时考虑了多种MR图像分割，其中，例如，可能是一个系列的问题经过一段时间的扫描相同的组织（的2D/3D）图像，图像的数量，或不同的切片图像的对称部分。 MR图像的多是分割份额常见的结构信息，因此他们可以协助彼此分割的程序。我们提出了一个贝叶斯共同分割算法在共享的信息整个图像是通过利用马尔可夫随机场前，和吉布斯采样后采样是有效的聘用。由于我们的共同拉动分割算法考虑到所有的图像信息的同时，它提供比个人更准确和坚实的结果分割，如支持从模拟和实际结果的例子。-Segmentation is one of the basic problems in MRI analysis.We consider the problem of simultaneously segmenting multiple MR images, which, for example, could be a series

of (2D/3D) images of the same tissue scanned over time,different slices of a volume image, or images of symmetric parts. The multiple MR images to be segmented share

common structure information and hence they are able to assist each other in the segmentation procedure. We propose a Bayesian co-segmentation algorithm where the shared information

across images is utilized via a Markov random field prior, and a Gibbs sampler is employed for efficient posterior sampling. Because our co-segmentation algorithm pulls

all the image information into consideration simultaneously,it provides more accurate and robust results than the individual

segmentation, as supported by results from both simulated and real examples.