Name
Abstract | ||
|---|---|---|
One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.(1) proposed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2043531 | Proceedings of SPIE |
Keywords | Field | DocType |
image segmentation | Point distribution model,Computer vision,Active shape model,Global optimization,Pattern recognition,Segmentation,One-to-one,Active appearance model,Image segmentation,Artificial intelligence,Probabilistic logic,Physics | Conference |
Volume | ISSN | Citations |
9034 | 0277-786X | 2 |
PageRank | References | Authors |
0.39 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Ehrhardt | 1 | 387 | 54.33 |
| Julia Krüger | 2 | 16 | 7.63 |
| Heinz Handels | 3 | 1527 | 239.84 |