Combining Methods For Mass Detection In Mammograms

This paper presents a combined system for detecting masses in mammographic images. The proposed approach analyses the mammograms in three major steps. First a global segmentation method is applied to find the regions of interest. This step uses texture features, decision trees and a multiresolution Markov Random Field model. The second stage works on the output of the previous algorithm. Here a combination of three different local segmentation methods is used, and then some relevant features are extracted. Some of them refer to the shape of the object; others are simple texture parameters. Based on these features the final decision is made.