Architectural distortion is an important and early sign of breast
cancer, but because of its subtlety, it is a common cause of
false-negative findings on screening mammograms. Screening
mammograms obtained prior to the detection of cancer could contain
subtle signs of early stages of breast cancer, in particular,
architectural distortion. This book presents image processing and
pattern recognition techniques to detect architectural distortion
in prior mammograms of interval-cancer cases. The methods are based
upon Gabor filters, phase portrait analysis, procedures for the
analysis of the angular spread of power, fractal analysis, Laws'
texture energy measures derived from geometrically transformed
regions of interest (ROIs), and Haralick's texture features. With
Gabor filters and phase-portrait analysis, 4,224 ROIs were
automatically obtained from 106 prior mammograms of 56
interval-cancer cases, including 301 true-positive ROIs related to
architectural distortion, and from 52 mammograms of 13 normal
cases. For each ROI, the fractal dimension, the entropy of the
angular spread of power, 10 Laws' texture energy measures, and
Haralick's 14 texture features were computed. The areas under the
receiver operating characteristic (ROC) curves obtained using the
features selected by stepwise logistic regression and the
leave-one-image-out method are 0.77 with the Bayesian classifier,
0.76 with Fisher linear discriminant analysis, and 0.79 with a
neural network classifier. Free-response ROC analysis indicated
sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs)
per image, respectively, with the Bayesian classifier and the
leave-one-image-out method. The present study has demonstrated the
ability to detect early signs of breast cancer 15 months ahead of
the time of clinical diagnosis, on the average, for interval-cancer
cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented
computer-aided detection techniques, dedicated to accurate
detection and localization of architectural distortion, could lead
to efficient detection of early and subtle signs of breast cancer
at pre-mass-formation stages.
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