A local mean and variance active contour model for biomedical image segmentation

Abstract

To effectively segment the biomedical images with intensity inhomogeneity, a local mean and variance (LMV) active contour model is presented in this paper. In the model, the distribution of intensity belonging to each region is assumed as a Gaussian distribution with spatially varying mean and variance. At the same time, an energy function is defined and incorporated into a variational level set formulation. The curve evolution equation is then obtained from energy minimization. Because both image local mean and variance are considered, the proposed model can more effectively deal with the images with intensity inhomogeneity than the existing active contour model. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the local binary fitting (LBF) model and the local hybrid image fitting (LHIF) model, our model is much most computationally efficient and much least sensitive to the initial contour.