Model-based detection of spiculated lesions in mammograms.
Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. In this paper we concentrate on the detection of spiculated lesions in mammograms. A spiculated lesion is typically characterized by an abnormal pattern of linear structures and a central mass. Statistical models have been developed to describe and detect both these aspects of spiculated lesions. We describe a generic method of representing patterns of linear structures, which relies on the use of factor analysis to separate the systematic and random aspects of a class of patterns. We model the appearance of central masses using local scale-orientation signatures based on recursive median filtering, approximated using principal-component analysis. For lesions of 16 mm and larger the pattern detection technique results in a sensitivity of 80% at 0.014 false positives per image, whilst the mass detection approach results in a sensitivity 80% at 0.23 false positives per image. Simple combination techniques result in an improved sensitivity and specificity close to that required to improve the performance of a radiologist in a prompting environment.
Zwiggelaar
R
R
Wolfson Image Analysis Unit, University of Manchester, UK. reyer@sis.port.ac.uk
Parr
T C
TC
Schumm
J E
JE
Hutt
I W
IW
Taylor
C J
CJ
Astley
S M
SM
Boggis
C R
CR
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003196:Computer Graphics; D005163:Factor Analysis, Statistical; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*