Active contours driven by grayscale morphology fitting energy for fast image segmentation

Abstract

An active contour model (ACM) based on grayscale morphology fitting energy for fast image segmentation in the presence of intensity inhomogeneity is proposed. The core idea of grayscale morphology fitting energy is using the grayscale erosion and dilation operations to fit the image intensities on the two sides of contours. By extracting local intensity information using morphological operators, the proposed model can effectively segment images with intensity inhomogeneity, and the computational cost is low because the grayscale morphology fitting functions do not need to be updated during the process of curve evolution. Experiments on synthetic and real images have shown that the proposed model can achieve accurate segmentation. In addition, it is more robust to the choice of initial contour and has a higher segmentation efficiency compared to traditional local fitting-based ACMs.