Image segmentation via active contour model driven by interregion contrast

Abstract. Interregion contrast is crucial for image segmentation, but it has not been explicitly incorporated into popular region-based active contour models, such as Chan–Vese model and region-scalable fitting model. We integrate the interregion contrast into the active contour model in a level set formulation. The major contribution of this paper is weighting the interregion contrast by a labeling function to define a difference energy. The labeling function controls the integration region of the difference energy. It enables the proposed method to be applicable to images with intensity inhomogeneity, since the high contrast between local regions on the two sides of the object boundary. Moreover, the major part of the data term can be computed out of iterations with the labeling function, which reduces much computation time. The integration of global and local contrast into the energy functional also accelerates the curve propagation. Experiments and comparisons conducted on synthetic and real images demonstrate both accuracy and efficiency of the proposed method.