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A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms


Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)–based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

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Correspondence to Sudipta Mukhopadhyay.

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Karale, V.A., Ebenezer, J.P., Chakraborty, J. et al. A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms. J Digit Imaging 32, 728–745 (2019).

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  • 2D NEO
  • Non-linear energy operator
  • NEO
  • Microcalcification
  • Microcalcification clusters
  • Mammogram
  • Shape features
  • Texture features
  • SVM classifier