摘要：Segmentation is one of the basic problems in magnetic resonance (MR) image analysis. We consider the problem of simultaneously segmenting multiple MR images, which, for example, can 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. These multiple MR images share common structure information and hence they can assist each other in the segmentation procedure. We propose a Bayesian co-segmentation algorithm where the shared information across multiple images is utilized via a Markov random field prior. An efficient algorithm based on the Swendsen–Wang method is employed for posterior sampling, which is more efficient than the single-site Gibbs sampler. Because our co-segmentation algorithm pulls all the image information into consideration, it provides more accurate and robust results than individual segmentation, as supported by our experimental studies with real examples.
