Multiscale connected chain topological modelling for microcalcification classification

Minu George, Zhili Chen, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

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Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47±1.30%, 90.0±0.00%, 82.5±2.63%, 76.75±0.66% for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.

Original languageEnglish
Article number103422
JournalComputers in Biology and Medicine
Early online date05 Sept 2019
Publication statusPublished - 01 Nov 2019


  • microcalcification
  • topological modelling
  • multiscale connected chains
  • benign/malignant classification
  • Benign/malignant classification
  • Topological modelling
  • Multiscale connected chains
  • Breast Diseases/diagnostic imaging
  • Breast/diagnostic imaging
  • Humans
  • Mammography/methods
  • Algorithms
  • Calcinosis/diagnostic imaging
  • Female
  • Radiographic Image Interpretation, Computer-Assisted/methods


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