Speeding up snakes

In this paper we summarize new and existing approaches for the semiautomatic image segmentation based on active contour models. In order to replace the manual segmentation of images of the medical research of the Center of Anatomy at the Georg August University of Göttingen we developed a user interface based on snakes. Due to the huge images (sometimes bigger than 100 megapixels) the research deals with, an efficient implementation is essential. We use a multiresolution model to achieve a fast convergence in coarse scales. The subdivision of an active contour into multiple segments and their treatment as open snakes allows us to exclude those parts of the contour from the calculation, which have already aligned with the desired curve. In addition, the band structure of the iteration matrices can be used to set up a linear algorithm for the computation of one single deformation step. Finally, we gained an acceleration of the initial computation of the Edge Map and the Gradient Vector Flow by the use of contemporary CPU architectures. Furthermore, the storage of huge images next to additional data structures, such as the Gradient Vector Flow, requires lots of memory. We show a possibility to save memory by a lossy scaling of the traditional potential image forces.