Improving active contours for segmentation and tracking of motile cells in videomicroscopy

We describe a method to segment and track motile biological cells in videomicroscopy data, based on parametric active contours. We propose improvements designed to overcome obstacles in applications to cellular imaging. To allow detection of low contrast pseudopodia, we use an edge map based on the local intensity deviation that better reflects cell/background boundaries. To allow correct segmentations of cells in contact, we introduce a repulsion effect between contours. The method performance is illustrated on real data.

[1]  Vannary Meas-Yedid,et al.  Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing , 2002, IEEE Transactions on Medical Imaging.

[2]  Jean-Christophe Olivo-Marin,et al.  Active contours for the movement and motility analysis of biological objects , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Thomas S. Huang,et al.  Image processing , 1971 .

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Johan Montagnat,et al.  Shape and Topology Constraints on Parametric Active Contours , 2001, Comput. Vis. Image Underst..

[7]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[8]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..