A gradient image describes the differences of neighboring pixels in the image. Extracting edges only depending on a gradient image will results in noised and broken edges. Here, we propose a two-stage edge extraction approach with contextual-filter edge detector and multiscale edge tracker to solve the problems. The edge detector detects most edges and the tracker refines the results as well as reduces the noised or blurred influence; moreover, the extracted results are nearly thinned edges which are suitable for most applications. Based on six wavelet basis functions, qualitative and quantitative comparisons with other methods show that the proposed approach extracts better edges than the other wavelet-based edge detectors and Canny detector extract.