Difference between revisions of "Seg EM"
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Revision as of 13:30, 3 October 2014
seg_EM is a general purpose intensity based image segmentation tool. In it's simplest form, it takes in one 2D or 3D image and segments it in n classes. This function has implemented several other improvements (Please click the hyperlinks to go to specific sections): • Masking for region-of-interest selection [link] • Input anatomical priors, either as a series of images or as a 4D image. These priors should be pre-registered [link] • Markov Random Field for spatial consistency [link] • Bias field inhomogeneity correction to compensate for B0 non-uniformities. The Bias corrected image can also be outputted [link] • Outlier detection as in Van Leemput et al. TMI (2003) [link] • Prior relaxation as in Cardoso et al. MICCAI (2011) [link] • Semi-conjugate prior over the model parameters as explained in Cardoso et al. MICCAI (2011) [link]
The help for this function can be obtained by running
seg_EM -h
