Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images
Cohen, Laurent D.; Benmansour, Fethallah (2009), Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images, MIA 2006, 2006-09, Paris, France
TypeCommunication / Conférence
Titre du colloqueMIA 2006
Date du colloque2006-09
Ville du colloqueParis
Pays du colloqueFrance
Nom de la revueJournal of Mathematical Imaging and Vision
MétadonnéesAfficher la notice complète
Résumé (EN)In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.
Mots-clésObject extraction; Digital topology; Surface meshing; Energy minimizing curves; Minimal paths; Image segmentation; Fast marching method
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