We propose a methodological framework for the classification of the tree-like structures of the ductal network of human breast regarding radiological findings related to breast cancer. Initially we perform the necessary preprocessing steps such as image segmentation in order to isolate the ductal tree structure from the background of x-ray galactograms. Afterwards, we employ tree characterization approaches to obtain a symbolic representation of the distribution of trees’ branching points. Our methodology is based on Sholl analysis, a technique which uses concentric circles that radiate from the center of the region of interest. Finally, we apply the k-nearest neighbor classification scheme to characterize the tree-like ductal structures in galactograms in order to distinguish among different radiological findings. The experimental results are quite promising as the classification accuracy reaches up to 82% indicating that our methods may assist radiologists to identify image biomarkers in galactograms.