title: A method for detecting microcalcifications in digital mammograms.
abstract: Microcalcification clusters are often an important indicator for the detection of malignancy in mammograms. In many cases, microcalcifications are the only indication of a malignancy. However, the detection of microcalcifications can be a difficult process. They are small and can be embedded in dense tissue. This paper presents a method for automatically detecting microcalcifications. We utilize a high-boost filter to suppress background clutter enabling segmentation even in very dense breast tissue. We then use a threshholding and region growing technique to extract candidate microcalcifications. Likely microcalcifications are then identified by a linear classifier. We apply this method to images selected from the LLNL/UCSF Digital Mammogram Library, and produce a receiver operating characteristic (ROC) curves to detail the trade-off between probability of detection and false alarms. Finally, we exam the ability to properly select a threshold to achieve a desired probability of detection based upon a training set. This is a US government work. There are no restrictions on its use.
authors.affiliation: Naval Surface Warfare Center Dahigren Division, Advanced Computation Technology Group, VA 22448, USA.
authors.forename: B C
authors.initials: BC
authors.lastname: Wallet
authors.forename: J L
authors.initials: JL
authors.lastname: Solka
authors.forename: C E
authors.initials: CE
authors.lastname: Priebe
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D012372:ROC Curve; D011856:Radiographic Image Enhancement