Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images

  • Authors:

  • Fethallah Benmansour;Laurent D. Cohen

  • Affiliations:

  • CEREMADE, UMR CNRS 7534, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, Paris Cedex 16, France 75775;CEREMADE, UMR CNRS 7534, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, Paris Cedex 16, France 75775

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.