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Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation
[chapter]
2010
Lecture Notes in Computer Science
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main
doi:10.1007/978-3-642-15711-0_10
fatcat:ihfll6y3jzdabk6fo3xwcznsne