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Abstract

We have developed a method for the quantification of breast texture by using different algorithms to classify mammograms into the four patterns described by Wolfe (N1, P1, P2 and Dy). The computerized scheme employs craniocaudal views of conventional screen-film mammograms, which are digitized by a laser scanner. We used discriminant analysis to select among different feature-extraction techniques, including Fourier transform, local-contrast analysis, and grey-level distribution and quantification. The method has been evaluated on 117 clinical mammograms previously classified by five radiologists as to mammographic breast parenchymal patterns (MBPPS). The results show differences in agreement among radiologists and computer classification, depending on the Wolfe pattern: excellent for Dy (kappa = 0.77), good for P2 (kappa = 0.52) and N1 (kappa = 0.52) and poor for P1 (kappa = 0.22). Our quantitative texture measure as calculated from digital mammograms may be valuable to radiologists in their assessment of MBPP and therefore useful in establishing an index of risk for developing breast carcinoma.

Prediction of Near-term Breast Cancer Risk Based on Bilateral Mammographic Feature Asymmetry

“…However, subjectively-rated mammographic density using BIRADS has been found unreliable due to considerable inter- and intra-observer variability among radiologists (23). In order to achieve more reliable and consistent mammographic density assessment results, a number of research groups have developed various computerized schemes to detect and quantify mammographic density (24-34). In these previous researches, different image features and machine learning-based classifiers were investigated and compared.…”

Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry

“…Since visual assessment of mammographic density into four BIRADS categories is difficult and often inaccurate due to the large inter-observer variability [10], a number of research groups have developed computerized algorithms and schemes to detect and quantify breast tissue density based on a variety of features including the image statistic features of the pixel values, such as mean, standard deviation, skewness, kurtosis, entropy, and the other higher order momentum based measures, computed from the original and/or processed images, the mathematical morphology and texture based features, such as power spectrum and fractal dimension [11–18]. For example, Zhou et al .…”

Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience

“…Many researchers have developed the computerized methods for the assessment of mammographic patterns [18][19][20][21][22]. In case of whole breast US images, computerized methods for assessing parenchymal patterns will probably be useful.…”