目的 边缘是图像最为重要的特征之一，是图象分析与识别的基础。对于目标的分割、测量而言，边缘提取的连续性与抗噪性显得尤为重要，其可通过区域增长等算法提取目标区域，为抠图、统计测量提供必要的支持，本文以实现目标轮廓的有效提取为目的，提出一种结合高斯加权距离图的图像边缘提取方法。方法 首先通过计算分块区域内像素间的高斯加权距离，获得高斯加权距离图，该图与原图相比，不仅可以较好地突出边缘轮廓，而且可以统一背景灰度。其次通过分析高斯加权距离图的灰度直方图，将灰度分为两类并计算类中心，以此作为无边缘活动轮廓（CV）模型的c₁和c₂参数，最后通过CV模型求解图像边缘。结果 与其他边缘提取算法相比，该算法不仅具有较好的抗噪性，同时可以保证图像边缘提取的连续性。结论 实验结果验证了本文算法的有效性。

Objective 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. Method 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 c₁ and c₂. Finally, edges of the image are found using the CV model. Result 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. Conclusion The experimental results demonstrate the effectiveness of the proposed algorithm.