PROCEEDINGS ARTICLE | March 9, 2010
Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis
KEYWORDS: Breast, Optical filters, Breast cancer, Receivers, Artificial neural networks, Image filtering, Fractal analysis, Mammography, Feature selection, Architectural distortion
This paper presents methods for the detection of architectural distortion in mammograms of interval-cancer cases
taken prior to the diagnosis of breast cancer, using Gabor filters, phase portrait analysis, fractal dimension (FD),
and analysis of the angular spread of power in the Fourier spectrum. In the estimation of FD using the Fourier
power spectrum, only the distribution of power over radial frequency is considered; the information regarding
the angular spread of power is ignored. In this study, the angular spread of power in the Fourier spectrum is
used to generate features for the detection of spiculated patterns related to architectural distortion. Using Gabor
filters and phase portrait analysis, a total of 4224 regions of interest (ROIs) were automatically obtained from
106 prior mammograms of 56 interval-cancer cases, including 301 ROIs related to architectural distortion, and
from 52 mammograms of 13 normal cases. For each ROI, the FD and measures of the angular spread of power
were computed. Feature selection was performed using stepwise logistic regression. The best result achieved,
in terms of the area under the receiver operating characteristic curve, is 0.75 ± 0.02 with an artificial neural
network including radial basis functions. Analysis of the performance of the methods with free-response receiver
operating characteristics indicated a sensitivity of 0.82 at 7.7 false positives per image.
