From Ziplock Snakes to Velcro^™ Surfaces

Abstract

The use of energy minimizing deformable models in various applications has become very popular. The issue of initializing such models, however, has not received much attention although the model’s performance depends critically on its initial state. We aim at obtaining good convergence and segmentation properties from a minimum of a priori information.

We present a new approach to segmentation of 2- and 3-Dimensional shapes that initializes and then optimizes a deformable model given only the data and a very small number of 2-D or 3-D seed points respectively. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In effect, the 2-D “snake” and the 3-D surface model are clamped onto the object boundary in manner reminiscent of a ziplock or velcro being closed.

We develop the method’s mathematic framework and show results using 2-D cartographic data. Preliminary results in 3-D using volumetric medical data are shown as well.