Title:
BayesianCoSegmentationOfMultipleMRImages
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Description: 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.
- [som] - MRI Brain Tumour Classification- SOM ( S
- [icm1] - Achieve ICM algorithm (iterative model c
- [gibbs] - use gibbs image segment.wish it will hel
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bayesian co-segmentation of multiple MR images.pdf
