Computer-aided detection of clustered microcalcifications on digital mammograms.
A computer-aided diagnosis scheme to assist radiologists in detecting clustered microcalcifications from mammograms is being developed. Starting with a digital mammogram, the scheme consists of three steps. First, the image is filtered so that the signal-to-noise ratio of microcalcifications is increased by suppression of the normal background structure of the breast. Secondly, potential microcalcifications are extracted from the filtered image with a series of three different techniques: a global thresholding based on the grey-level histogram of the full filtered image, an erosion operator for eliminating very small signals, and a local adaptive grey-level thresholding. Thirdly, some false-positive signals are eliminated by means of a texture analysis technique, and a non-linear clustering algorithm is then used for grouping the remaining signals. With this method, the scheme can detect approximately 85% of true clusters, with an average of two false clusters detected per image.
Nishikawa
R M
RM
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
Giger
M L
ML
Doi
K
K
Vyborny
C J
CJ
Schmidt
R A
RA
D001941:Breast Diseases / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*