Loop-free snakes for highly irregular object shapes
`Snakes' are an effective approach to image segmentation. However, self-looping is a common problem that can cause segmentation failure of snakes in the recovery of highly irregular object shapes, such as in long tube-like shapes, sharp corners or deep concave/convex shapes. This paper introduces the notion of loop-free snakes that can quickly and effectively remove all self-loops during their evolution, consistently deforming and conforming to complicated shapes of target objects. The proposed snakes are less sensitive to their initial contour condition, are resilient to their inconsistent parameter settings in a certain degree and require low computing cost in terms of both computation time and storage. Experiments are conducted to segment real images with encouraging results.
snakes segmentation deformable model self-looping suppression topological adaptability