Noise-enhanced detection of micro-calcifications in digital mammograms

Renbin Peng, Hao Chen, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The appearance of micro-calcifications in mammograms is a crucial early sign of breast cancer. Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task. This paper presents novel algorithms for the detection of micro-calcifications using stochastic resonance (SR) noise. In these algorithms, a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. First, a SR noise-based detection approach is presented to improve some suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a SR noise-based detection enhancement framework is presented to deal with more general model mismatch cases. Our algorithms and the framework are tested on a set of 75 representative abnormal mammograms. They yield superior performance when compared with several classification and detection approaches developed in our work as well as those available in the literature.

Original languageEnglish (US)
Pages (from-to)62-73
Number of pages12
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number1
StatePublished - 2009


  • Detection of micro-calcifications
  • Digital mammograms
  • Stochastic resonance (SR) noise

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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