Name
Title | ||
|---|---|---|
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation. |
Abstract | ||
|---|---|---|
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 overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15711-0_10 | MICCAI (3) |
Keywords | Field | DocType |
manual segmentation,segmentation framework,non-parametric iterative model constraint,new non-parametric model constraint,automatic kidney segmentation,kidney segmentation,latent model,method yield,fixed parametric prior model,intensity model,graph min-cut,ground-truth segmentation,ground truth,parametric model,expectation maximization,latent variable | Computer vision,Scale-space segmentation,Pattern recognition,Markov random field,Expectation–maximization algorithm,Computer science,Segmentation,Constraint graph,Image segmentation,Parametric statistics,Artificial intelligence,Energy functional | Conference |
Volume | Issue | ISSN |
13 | Pt 3 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15710-6 | 21 | 0.95 |
References | Authors | |
12 | 5 | |