Ultrasound image segmentation with shape priors : application to automatic cattle rib-eye area estimation
Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | Uruguay |
| Institución: | Universidad de la República |
| Repositorio: | COLIBRI |
| Idioma: | inglés |
| OAI Identifier: | oai:colibri.udelar.edu.uy:20.500.12008/38758 |
| Acceso en línea: | https://hdl.handle.net/20.500.12008/38758 |
| Access Level: | acceso abierto |
| Palabra clave: | Rib-eye Shape priors Ultrasound (US) segmentation Procesamiento de Señales |
| Sumario: | Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts. |
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