In this paper

we propose a local statistical geodesic active contour (GAC) image segmentation method. Local intensity statistical information according with the Gaussian distribution is assumed. A directional driving item is established in order to reduce the effect of intensity inhomogeneity information. Second

a local statistical geodesic active contour energy function based on this hypothesis is established. By minimizing the proposed energy functional

it can orderly guide the movement of evolution curve to object boundaries for achieving regions of interest (ROI) segmentation. Finally

the method is implemented by a binary level set function in order to improve the algorithm's efficiency and stability. Experiment results with medical images show that the algorithm can segment ROI of medical images fast and accurate.