Improving the robustness of gradient vector flowin cluttered images

The gradient vector flow (GVF) algorithm has been extensively used as a tool to extract the boundary of objects in images. However, its performance significantly degrades when the images have a cluttered background i.e., other structures in the vicinity of the object of interest. In this case, the elastic contour is often attracted towards invalid features. This paper discusses the robustness of GVF and proposes a way to improve its performance. The main difference concerns the use of more robust and informative features (middle level features) which significantly reduce the influence of noise. Experiments are presented to illustrate the performance of the improved GVF algorithm.