Automated detection of microcalcifications in mammograms through application of image pixel remapping and statistical filter

Mark Wilson, Roger Hargrave, Sunanda Mitra, Yao Yang Shieh, Glenn H. Roberson

Research output: Contribution to journalConference article

11 Scopus citations

Abstract

Mammography and finding of suspicious masses during self-examinations and clinical breast examinations form the primary screening tools for early detection of breast cancer. Mammography is essential for early detection of cancer, prior to any other means. Many abnormalities other than cancer are shown by screening mammography, leading to biopsy of the suspicious defect. Any enhancement that reduces the number of unnecessary biopsy is welcomed. Digital enhancement may aid in early detection of some patterns such as microcalcification clusters indicating onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers and early detection may enable complete cure. The individual calcifications are hard to detect due to size and shape variability and inhomogeneous background structure. Our study addresses only early detection of microcalcifications. We present an algorithm, which locates microcalcifications based on local grayscale variability, tissue structures, and image statistics. The mammographs are digitally enhanced to accent textures and a previously developed threshold filter creates a binary mask of the calcification spatial location.

Original languageEnglish
Pages (from-to)270-274
Number of pages5
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
StatePublished - 1998
EventProceedings of the 1998 11th IEEE Symposium on Computer-Based Medical Systems - Lubbock, TX, USA
Duration: Jun 12 1998Jun 14 1998

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