Efficient Shape Priors for Spline-Based Snakes.
Parametric active contours are an attractive approach for image segmentation, thanks to their computational efficiency. They are driven by application-dependent energies that reflect the prior knowledge on the object to be segmented. We propose an energy involving shape priors acting in a regularization-like manner. Thereby, the shape of the snake is orthogonally projected onto the space that spans the affine transformations of a given shape prior. The formulation of the curves is continuous, which provides computational benefits when compared with landmark-based (discrete) methods. We show that this approach improves the robustness and quality of spline-based segmentation algorithms, while its computational overhead is negligible. An interactive and ready-to-use implementation of the proposed algorithm is available and was successfully tested on real data in order to segment Drosophila flies and yeast cells in microscopic images.
Ricard
R
Delgado-Gonzalo
Daniel
D
Schmitter
Virginie
V
Uhlmann
Michael
M
Unser
D000465:Algorithms; D000818:Animals; D004330:Drosophila / Q000033:anatomy & histology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D012568:Schizosaccharomyces / Q000166:cytology