Computerized texture analysis of mammographic images has emerged as a means to characterize breast parenchyma
and estimate breast percentage density, and thus, to ultimately assess the risk of developing breast cancer. However,
during the digitization process, mammographic images may be modified and optimized for viewing purposes, or
mammograms may be digitized with different scanners. It is important to demonstrate how computerized texture
analysis will be affected by differences in the digital image acquisition. In this study, mammograms from 172
subjects, 30 women with the BRCA1/2 gene-mutation and 142 low-risk women, were retrospectively collected and
digitized. Contrast enhancement based on a look-up table that simulates the histogram of a mixed-density breast
was applied on very dense and very fatty breasts. Computerized texture analysis was performed on these
transformed images, and the effect of variable gain on computerized texture analysis on mammograms was
investigated. Area under the receiver operating characteristic curve (AUC) was used as a figure of merit to assess
the individual texture feature performance in the task of distinguishing between the high-risk and the low-risk
women for developing breast cancer. For those features based on coarseness measures and fractal measures, the
histogram transformation (contrast enhancement) showed little effect on the classification performance of these
features. However, as expected, for those features based on gray-scale histogram analysis, such as balance and
skewnesss, and contrast measures, large variations were observed in terms of AUC values for those features.
Understanding this effect will allow us to better assess breast cancer risk using computerized texture analysis.