Pull-push level sets: a new term to encode prior knowledge for the segmentation of teeth images

Rodrigo de Luis Garcia; Raul San Jose Estepar; Carlos Alberola-Lopez

doi:10.1117/12.594060

29 April 2005 Pull-push level sets: a new term to encode prior knowledge for the segmentation of teeth images

Rodrigo de Luis Garcia, Raul San Jose Estepar, Carlos Alberola-Lopez

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594060
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

This paper presents a novel level set method for contour detection in multiple-object scenarios applied to the segmentation of teeth images. Teeth segmentation from 2D images of dental plaster cast models is a difficult problem because it is necessary to independently segment several objects that have very badly defined borders between them. Current methods for contour detection which only employ image information cannot successfully segment such structures. Being therefore necessary to use prior knowledge about the problem domain, current approaches in the literature are limited to the extraction of shape information of individual objects, whereas the key factor in such a problem are the relative positions of the different objects composing the anatomical structure. Therefore, we propose a novel method for introducing such information into a level set framework. This results in a new energy term which can be explained as a regional term that takes into account the relative positions of the different objects, and consequently creates an attraction or repulsion force that favors a determined configuration. The proposed method is compared with balloon and GVF snakes, as well as with the Geodesic Active Regions model, showing accurate results.

Citation Download Citation

Rodrigo de Luis Garcia, Raul San Jose Estepar, and Carlos Alberola-Lopez "Pull-push level sets: a new term to encode prior knowledge for the segmentation of teeth images", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594060

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