Most of edge extraction techniques are local operators, thus providing only local information without providing any structural information. Therefore edge points themselves are not adequate as primitive descriptors in computer vision, and local edge points need to be linked into long, straight or slowly curving, line segments. In this paper, a simple and efficient curvilinear feature extraction algorithm using minimum spanning trees is described. The new algorithm is based on the minimum spanning trees found from the edge points. The purpose of finding minimum spanning trees is to link edge points, thus filling gaps and providing structural information. An approximation technique which transforms curvilinear features into straight lines is also described.