Modelling mammographic microcalcification clusters using persistent mereotopology

Harry George Strange, Zhili Chen, Erika R. E. Denton, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

29 Citations (SciVal)
180 Downloads (Pure)


In mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such microcalcification clusters. In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases.
Original languageEnglish
Pages (from-to)157-163
Number of pages7
JournalPattern Recognition Letters
Early online date01 Jul 2014
Publication statusPublished - 01 Oct 2014


Dive into the research topics of 'Modelling mammographic microcalcification clusters using persistent mereotopology'. Together they form a unique fingerprint.

Cite this