A biologically inspired algorithm for microcalcification cluster detection.
The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. We introduce a novel method for microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme.
Linguraru
Marius George
MG
University of Oxford, Medical Vision Laboratory, Oxford, UK. mglin@deas.harvard.edu
Marias
Kostas
K
English
Ruth
R
Brady
Michael
M
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002114:Calcinosis / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008327:Mammography / Q000706:statistics & numerical data; D012372:ROC Curve