Classification of microcalcification clusters based on morphological topology analysis

Authors:
Zhili Chen;Erika R. E. Denton;Reyer Zwiggelaar
Affiliations:
Department of Computer Science, Aberystwyth University, Aberystwyth, UK,Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China;Department of Radiology, Norfolk and Norwich University Hospital, Norwich, UK;Department of Computer Science, Aberystwyth University, Aberystwyth, UK
Venue:
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Year:
2012

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Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines

Artificial Intelligence in Medicine

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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.