Modeling of Front Evolution with Graph Cut Optimization
In this paper, we present a novel active contour model, in which the traditional gradient descent optimization is replaced by graph cut optimization. The basic idea is to first define an energy function according to curve evolution and then construct a graph with well selected edge weights based on the objective energy function, which is further optimized via graph cut algorithm. In this fashion, our model shares advantages of both level set method and graph cut algorithm, which are "topological" invariance, computational efficiency, and immunity to being stuck in the local minima. The model is validated on synthetic images, applied to two-class segmentation problem, and compared with the traditional active contour to demonstrate effectiveness of the technique. Finally, the method is applied to samples imaged with transmission electron microscopy that demonstrate complex textured patterns corresponding subcellular regions and micro-anatomy.