Minimization of Region-Scalable Fitting Energy for Image Segmentation

TLDR

Intensity inhomogeneities frequently occur in real‑world images and hinder accurate segmentation. The study proposes a region‑based active contour model that uses local intensity information at a controllable scale to mitigate these inhomogeneities. The model defines a data‑fitting energy based on two local fitting functions, incorporates it into a variational level‑set framework with regularization, and derives an evolution equation that uses a kernel to guide contour motion while preserving level‑set smoothness. Experiments on synthetic and real images demonstrate that the method achieves desirable segmentation performance.

Abstract

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.