Finding mamographic masses located in a dense breast tissue is a challenge even for an experienced radiologist. The difficulty comes from the similarity of intensity between the masses and the overlapped normal dense tissues. In this study, a novel method for classification of masses localized in dense background of breast is proposed. The method can identify meaningful superpixel patterns present in mammograms within mass-like regions. The topology of superpixel patterns, captured by using spatial connectivity graphs, revealed significant differences between cancerous and healthy areas of breasts. Four clinically recognizable features were extracted from the superpixel graphs and used for classification. The proposed approach was evaluated using ninety three dense ROIs selected from the publicly available Digital Database for Screening Mammography (DDSM). All 93 ROIs were localized in dense backgrounds of breasts. Among them, 41 contained malignant masses in dense backgrounds and 52 contained healthy dense breast tissues. The results indicate that the graph features generated from superpixel pattern graphs can produce very effective and efficient feature descriptors of breast masses localized in dense background. Using Fisher Linear Discriminant Analysis (LDA) classifier AUC score of 0.90 was achieved for the four dimensional feature vector.