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MICCAI
2010
Springer

Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation

8 years 4 months ago
Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation
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 advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric ove...
Moti Freiman, A. Kronman, S. J. Esses, Leo Joskowi
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where MICCAI
Authors Moti Freiman, A. Kronman, S. J. Esses, Leo Joskowicz, Jacob Sosna
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