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Liang, Z. (1993). Tissue classification and segmentation of MR images. IEEE Engineering in Medicine and Biology. March, pp 81--85.

  author = 	 {Liang, Zhengrong},
  title = 	 {Tissue classification and segmentation of MR images},
  journal = 	 {IEEE Engineering in Medicine and Biology},
  year = 	 {1993},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTpages = 	 {81--85},
  OPTmonth = 	 {March},
  OPTnote = 	 {},
  OPTannote = 	 {}

Author of the summary: Jim Davies, 2006, jim@jimdavies.org

Cite this paper for:

Tissue classification of MR images group image elements and refer to the groups as classes.

Segmentation: Assignment of labels to each voxel (3D pixel) in an image.

Doing this automatically often is done through comparison to a model.

Validation or classification is the determination of the appropriate number of classes.

The two main kinds of segmentation:

Textures are problematic for these methods. Edge detectors will create edges in a texture, and region ID methods will make little regions for parts of textures. A way to avoid this is to characterize each voxel by the texture properties of its neighbor.

This paper then describes in detail an unsupervised method that assumes the values of voxels belonging to an image class the follows a statistical distribution and that all voxels fit a finite mixture. It fits voxel values to the mixture using the ML (maximum likelihood) principle, and the number of classes using information criteria.


Summary author's notes:

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