Evaluation of Graph Topological Features in Digitized Mammogram for Microcalcification Cluster Classification

Abstract

In this paper, the scale-specific graph topological changes of Microcalcifications (MC) were investigated to classify MC cluster. A series of multi-scale MC cluster graphs were generated based on the connectivity of individual MCs. The extracted features from the graph series were integrated with the statistical and morphological characteristics of MC clusters. Subsequent feature selection showed that the features related to the denseness of MC cluster at some specific scales of the generated graphs discriminated better than all other features in classifying MC clusters while using an ensemble classifier with 10-fold cross validation. The proposed method was evaluated using two well-known digitized datasets: MIAS (Mammographic Image Analysis Society) and DDSM (The Digital Database for Screening Mammography). High classification accuracy (around 98%) and good ROC (receiver operating characteristic) results (area under the ROC curve up to 0.99) were achieved.