Segmentation with Active Contours
Fabien Pierre, Mathieu Amendola, Clémence Bigeard, Timothé Ruel, Pierre-Frédéric Villard
Fabien Pierre, Mathieu Amendola, Clémence Bigeard, Timothé Ruel, and Pierre-Frédéric Villard, Segmentation with Active Contours, Image Processing On Line, 11 (2021), pp. 120–141.

Communicated by Luis Álvarez and Pascal Monasse
Demo edited by Pascal Monasse


Active contours (also known as snakes) have shown their ability to introduce regularity on image segmentation. In contrast with level-set approaches, the active contours techniques based on a contour parameterization are able to maintain the initial topology of the area of interest. For this reason, it has been used in recent medical research for diaphragm segmentation. Most of the on-line codes for 2D/3D segmentation, as well as built-in Matlab toolboxes are based on level-set methods. Moreover, in the literature, the implementation details of active contours methods with meshes in three dimensions are tight, making tedious any reproduction of these techniques. In this paper, we give some details of the implementation of active contours in 2D/3D with meshes, especially about the choice of the use of a 2D/3D mesh and its refinement. We also explore the choice of the parameters with a quantitative study of their influence on the segmentation results. The 3D segmentation method has been applied to CT scan images of the lungs.