A hybrid active contour model based on global and local information for medical image segmentation

For segmenting medical images with abundant noise, blurry boundaries, and intensity heterogeneities effectively, a hybrid active contour model that synthesizes the global information and the local information is proposed. A novel global energy functional is constructed, together with an adaptive weight by the statistical information of image pixels on the clustering idea. Minimizing this global energy functional in a variational level set formulation will drive the curve to desirable boundaries. The local energy functional contains the local threshold, which is used to correct the deviation of the level set function. Experiments demonstrate that the proposed method can segment synthetic and medical images effectively, and have a relatively higher performance compared to other representative methods.