Name
Title | ||
|---|---|---|
Volumetric segmentation of multiple basal ganglia structures using nonparametric coupled shape and inter-shape pose priors |
Abstract | ||
|---|---|---|
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images and present a quantitative performance analysis. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISBI.2009.5192975 | ISBI |
Keywords | Field | DocType |
segmentation accuracy,volumetric segmentation,prior density,anatomical structure,posteriori estimation problem,human brain exhibit co-dependencies,estimation framework,active contour-based iterative segmentation,segmentation problem,simultaneous volumetric segmentation,multiple basal ganglia structure,shape,kernel density estimation,estimation,kernel,moments,training data,magnetic resonance,kernel density estimate,iterative methods,maximum likelihood estimation,image segmentation,biomedical imaging,active contour | Active contour model,Computer vision,Multivariate kernel density estimation,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Maximum a posteriori estimation,Kernel density estimation | Conference |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mustafa Uzunbas | 1 | 54 | 5.39 |
| Octavian Soldea | 2 | 150 | 11.96 |
| Müjdat Çetin | 3 | 1342 | 112.26 |
| Gozde Unal | 4 | 405 | 18.32 |
| Aytül Erçil | 5 | 112 | 11.11 |
| Devrim Unay | 6 | 69 | 10.38 |
| Ahmet Ekin | 7 | 82 | 12.08 |
| Zeynep Firat | 8 | 9 | 2.52 |