Variant of the region-scalable fitting energy for image segmentation

This paper presents a variant of the level set function based on region-scalable fitting (RSF) model for segmenting a given image into different parts. In consideration of the image local characteristics, the RSF model can efficiently and effectively segment images with intensity inhomogeneity. Instead of utilizing n level set functions to define up to 2n phases in the RSF model, our method presents a piecewise constant level set formulation for image segmentation and each phase is represented by a unique constant value. In addition, our model avoids different segmentation results caused by different initializations. The energy functional of our method is locally differentiable and convex because we do not use the nondifferentiable Heaviside and Delta functions. Comparative experiment results demonstrate that our method is much more computationally efficient. Moreover, our algorithm is robust against destructive noise.