An active contour model driven by anisotropic region fitting energy for image segmentation

A novel region active contour model (ACM) for image segmentation is proposed in this paper. In order to perform an accurate segmentation of images with non-homogeneous intensity, the original region fitting energy in the general region-based ACMs is improved by an anisotropic region fitting energy to evolve the contour. Using the local image information described by the structure tensor, this new region fitting energy is defined in terms of two anisotropic fitting functions that approximate the image intensity along the principal directions of variation of the intensity. Therefore, the anisotropic fitting functions extract intensity information more precisely, which enable our model to cope with the boundaries with low-contrast and complicated structures. It is incorporated into a variational formula with a total variation (TV) regularization term with respect to level set function, from which the segmentation process is performed by minimizing this variational energy functional. Experiments on the vessel and brain magnetic resonance images demonstrate the advantages of the proposed method over Chan-Vese (CV) active contours and local binary active contours (LBF) in terms of both efficiency and accuracy.