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.
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Chen, Z., Denton, E.R.E., Zwiggelaar, R. (2012). Classification of Microcalcification Clusters Based on Morphological Topology Analysis. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_67
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DOI: https://doi.org/10.1007/978-3-642-31271-7_67
Publisher Name: Springer, Berlin, Heidelberg
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