Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly
missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application
of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms
taken prior to the detection of breast cancer. A method based on Gabor filters and phase portrait analysis was
used to detect initial candidates of sites of architectural distortion. A total of 386 regions of interest (ROIs) were
automatically obtained from 14 "prior mammograms", including 21 ROIs related to architectural distortion.
The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. The
average fractal dimension of the normal (false-positive) ROIs was higher than that of the ROIs with architectural
distortion. For the "prior mammograms", the best receiver operating characteristics (ROC) performance achieved
was 0.74 with the fractal dimension and 0.70 with fourteen texture features, in terms of the area under the ROC
curve.
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