Difference between revisions of "Seg EM"
From CMIC
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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): | 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): | ||
| − | + | * Input anatomical priors, either as a series of images or as a 4D image. | |
| − | + | ** These priors should be pre-registered using for example [http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg NiftyReg]. | |
| − | + | * Markov Random Field for spatial consistency | |
| − | + | * Bias field inhomogeneity correction to compensate for B0 non-uniformities as per [http://nmr.mgh.harvard.edu/~koen/VanLeemputTMI1999a.pdf Van Leemput et al. TMI (1999) ]. | |
| − | + | ** The Bias corrected image can also be outputted | |
| − | + | * Outlier detection as in [http://nmr.mgh.harvard.edu/~koen/VanLeemputTMI2001.pdf Van Leemput et al. TMI (2001)] | |
| − | + | * Prior relaxation as in [http://www.ncbi.nlm.nih.gov/pubmed/22906793 Cardoso et al. MICCAI (2011)] | |
| − | + | * Semi-conjugate prior over the model parameters as explained in [http://www.ncbi.nlm.nih.gov/pubmed/22906793 Cardoso et al. MICCAI (2011)] | |
The help for this function can be obtained by running | The help for this function can be obtained by running | ||
seg_EM -h | seg_EM -h | ||
Latest revision as of 16:50, 8 June 2016
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):
- Input anatomical priors, either as a series of images or as a 4D image.
- These priors should be pre-registered using for example NiftyReg.
- Markov Random Field for spatial consistency
- Bias field inhomogeneity correction to compensate for B0 non-uniformities as per Van Leemput et al. TMI (1999) .
- The Bias corrected image can also be outputted
- Outlier detection as in Van Leemput et al. TMI (2001)
- Prior relaxation as in Cardoso et al. MICCAI (2011)
- Semi-conjugate prior over the model parameters as explained in Cardoso et al. MICCAI (2011)
The help for this function can be obtained by running
seg_EM -h
