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Hierarchical Atlas Based EM Segmentation

Hierarchical Atlas Based EM Segmentation. Expectation Maximization Segmentation. Iterative method consisting of two steps : Expectation Step: Given the current Bias Field estimation, the E-step computes the likelihood of each tissue class.

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Hierarchical Atlas Based EM Segmentation

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  1. Hierarchical Atlas Based EM Segmentation

  2. Expectation Maximization Segmentation • Iterative method consisting of two steps : • Expectation Step: Given the current Bias Field estimation, the E-step computes the likelihood of each tissue class. • Maximization Step: Given the likelihood of each tissue class, the M-step estimates the image homogeneities. • Does not incorporate any spatial information for the classification of a voxel.

  3. Atlas Based EM Segmentation • Atlas provides the likelihood of a tissue class to be in a certain location.

  4. Atlas Generation • Create an atlas for one template subject based on N previous segmentations. • Co-register the “template” subject with the subject under study and apply the transformation to the different atlases. • Registration is a non linear warp developed by Alexandre Guimond.

  5. Hierarchical Model for Segmentation • Allows for more flexibility in the design as parameters can be set differently at each level of the hierarchical tree. • We have three parameters per node: • Weight of Input data (e.g. 40% spgr and 60% T2) • Weight of Atlas. • Weight of Tissue class.

  6. Summary EMSegmenter Registration

  7. Results for 5 cases

  8. References • A. Guimond, A. Roche, N. Ayache, and J. Meunier. Three-dimensional multimodal brain warping using the demons algorithm and adaptive intensity corrections. IEEE Transactions in Medical Imaging, 20(1):58-69, January 2001. • K. M Pohl, W. M. Wells, A. Guimond, K. Kasai, M. E. Shenton, R. Kikinis, W. E. L. Grimson and S. K. Warfield: Incorporating Non-Rigid Registration into Expectation Maximization Algorithm to Segment MR Images, Fifth International Conference on Medical Image Computing and Computer Assisted Intervention, Tokyo, Japan, September 25-28, 2002, pp. 564-572. • Kilian M. Pohl, Sylvain Bouix, Ron Kikinis and W. Eric L. Grimson, Anatomically Guided Segmentation with non Stationary Tissue Class Distributions in an Expectation Maximization Framework. Submitted to the International Symposium on Biomedical Imaging.

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