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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.
Originalsprache | Englisch |
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Titel | Medical Imaging 2014: Image Processing |
Redakteure/-innen | Sebastien Ourselin, Martin A. Styner |
Band | 9034 |
Herausgeber (Verlag) | SPIE |
Erscheinungsdatum | 21.03.2014 |
Seiten | 90340U |
ISBN (Print) | 9780819498274 |
DOIs | |
Publikationsstatus | Veröffentlicht - 21.03.2014 |
Veranstaltung | SPIE Medical Imaging 2014, Image Processing - San Diego, USA / Vereinigte Staaten Dauer: 15.02.2014 → 20.02.2014 https://spie.org/about-spie/press-room/mi14-news |
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Probabilistische statistische Form- und Appearance-Modelle zur robusten Multi-Objekt-Segmentierung in medizinischen Bilddaten
01.10.06 → 30.09.15
Projekt: DFG-Projekte › DFG Einzelförderungen