Classification of microcalcification clusters based on morphological topology analysis

Abstract

The presence of microcalcification clusters is a primary sign of breast cancer. It is difficult and time consuming for radiologists to diagnose microcalcifications. In this paper, we present a novel method for classification of malignant and benign microcalcification clusters in mammograms. We analyse the connectivity/topology between individual microcalcifications within a cluster using multiscale morphology. A microcalcification graph is constructed to represent the topological structure of clusters. A multiscale topological feature vector is generated by extracting two microcalcification graph properties. The validity of the proposed method is evaluated using a dataset taken from the MIAS database. The performance of including SFS feature selection is investigated. Using a k-nearest neighbour classifier, a classification accuracy of 95% and an area under the ROC curve of 0.93 are achieved. A comparison with existing approaches is presented. © 2012 Springer-Verlag Berlin Heidelberg.