Mass classification of mammograms using fractal dimensions and statistical features

For classification of tumors in mammography, the major features are extracted from the segmented tumor. However, some details of the tumor margin, such as the spiculated parts, are eliminated in the segmentation step. The current study suggests a new approach for extracting the spiculated parts and tumor core. The proposed method segments the tumor by assessing the similarity of the pixels of the tumor core and dissimilarity of the spiculated parts. Then, the spiculated parts and the tumor core are combined to create the final segmentation. Next, the statistical features and fractal dimensions are extracted from the tumor. The fractal dimension is a measure of complexity of the tumor shape that is effective for discriminating between benign and malignant tumors. The simulation results show that the proposed method is more suitable than other methods. The area under the ROC curve and the accuracy of the proposed method on mini-MIAS were 0.9627 and 89.66% and for DDSM were 0.9777 and 93.50%, respectively. The results confirm the efficiency of the proposed method for extracting the mass core and spiculated parts. They also show that use of the fractal dimension increases the accuracy of classification and complements the other shape features.