Edges are one of the most important features of an image

they are the basis of many image analysis and recognition techniques. The continuity and noise immunity of the edge extraction is particularly important for the segmentation and measurement. Regional growth algorithms can be used to extract the target area. They can provide the nece-ssary support for the matting and statistical measurement. For the purpose of effective contour extraction

we propose a method of image edge extraction combined with a Gaussian weighted distance graph in this paper. First

by calculating the distance between the pixels within the sub-block regions

the graph of Gaussian weighted distances is obtained. Comparing with the original figure

it not only can better highlight the edge contour

but also can get a uniformed background gray. Second

by analyzing the histogram of the Gaussian weighted distance

the gray values can be divided into two classes

each class center is calculated for active contour without edge (CV) parameters of and . Finally

edges of the image are found using the CV model. Comparing with other edge extraction algorithms

the proposed algorithm not only has better noise immunity

but also can guarantee the continuity of the image edge extraction. The experimental results demonstrate the effectiveness of the proposed algorithm.