Level Set Based Segmentation Using Local Feature Distribution

We propose a level set based framework to segment textured images. The snake deforms in the image domain in searching for object boundaries by minimizing an energy functional, which is defined based on dynamically selected local distribution of orientation invariant features. We also explore the user initialization to simplify the segmentation and improve accuracy. Experimental results on both synthetic and real data show significant improvements compared to direct modeling of filtering responses or piecewise constant modeling.