Detection of clustered microcalcifications in small field digital mammography

Tomasz Arodź, Marcin Kurdziel, Tadeusz J. Popiela, Erik O.D. Sevre, David A Yuen

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.

Original languageEnglish (US)
Pages (from-to)56-65
Number of pages10
JournalComputer Methods and Programs in Biomedicine
Issue number1
StatePublished - Jan 2006

Bibliographical note

Funding Information:
We thank Ben Holtzman, Lilli Yang and Katya Shukh for their artistic rendering. This research has been supported by a grant from the University of Minnesota Digital Technology Center, grant on wavelet analysis from CMG of National Science Foundation, the Polish State Committee for Scientific Research (KBN) research grant no. 3 T11C 059 26 and the Polish Ministry of Science and Information Society Technologies grant no. 3 T11F 019 29.


  • 2D filtering
  • Mammogram analysis
  • Microcalcification detection
  • Wavelets


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