A semantic annotation approach for calcifications in mammogram using Bayesian network model

To realize the medical semantic annotation of mammogram, a semantic modeling approach for calcifications in mammogram based on hierarchical Bayesian network was proposed. Firstly, support vector machines was used to map low-level image feature into feature semantics, then high-level semantic was captured through feature semantic fusion using Bayesian network, finally semantic model was established. To validate the method, the model was applied to annotate the semantic information of mammograms. In this experiment, we chose 142 images as training set and 50 images as testing set, the results showed that the precision ratio of malignant samples is 81.48%, and benign samples is 73.91%.