Network structures in images, resembling graphs with nodes and edges, are easily recognized by humans but challenging to extract computationally. Traditional methods for road extraction involve thinning semantic segmentation of road network images, followed by curve segmentation and linear approximation to form a graph. These methods struggle with image interference and lack precision in identifying intersections and turns. Supervised machine learning shows promise but requires extensive sample creation and labeling. This study introduces a conventional computer vision method using variable-sized windows for extracting topological structures from binary network images, demonstrating strong resistance to interference. We validated our method through a comparative analysis with a Harris corner detection approach, using binary road structure images from Hangzhou, China. Our results highlight the superior anti-interference capabilities of the variable window recognition method, especially in node recognition, compared to the Harris corner detection method.