OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.
OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.