Stereo matching is a hot topic in computer vision, while stereo matching in large textureless regions and slanted planes are still challenging problems. We propose a novel stereo matching algorithm to handle the problems. We novelly utilizes minimum spanning tree (MST) to construct a new superpixel-based neighboring system. The neighboring system is used to improve the matching performance in textureless regions. Then we apply the new neighboring system to the stereo matching problem, which uses the superpixel as the matching primitive. The use of the new neighboring system is efficient and effective. We compare our method with 4 popular methods. Experiments on Middlebury dataset show that our method can achieve good matching results. Especially, our method obtains more accurate disparity in textureless regions while maintaining a comparable performance of matching in slanted planes.