Surface Function Actives

Deformable models have been widely used in image segmentation since the introduction of the snakes. Later the introduction of level set frameworks to solve the energy minimization problem associated with the deformable model overcame some limitations of the parametric active contours with respect to topological changes by embedding surface representations into higher dimensional functions. However, this may also bring in more computational load so that recent advances in spatio-temporal resolutions of 3D/4D imaging raised some challenges for real-time segmentation, especially for interventional imaging. In this context, a novel segmentation framework, Surface Function Actives (SFA), is proposed for real-time segmentation purpose. SFA has great advantages in terms of potential efficiency, based on its dimensionality reduction for the surface representation. Utilizing implicit representations with variational framework also provides flexibility and benefits currently shared by level set frameworks. An application for minimally-invasive intervention is shown to illustrate the potential applications of this framework.