An image segmentation method based on structural pattern recognition is presented. Two graphs are generated from the image to be segmented. A model graph is generated from an oversegmentation of the image and from traces provided by the user. An input graph is generated from the oversegmented image. Image segmentation is then obtained by matching the input graph to the model graph. An objective function is defined and optimized using a new approach to find the most suitable clique of the corresponding association graph. The structural information encoded in the graphs leads to a robust segmentation performance even in the case of non-homogeneous textured regions. Successful experimental results obtained from real images are provided.