Abstract
High mammographic density is associated with reduced sensitivity of mammography. Recent changes in the BI-RADS density assessment address the potential for dense tissue to mask lesions, but the assessment remains qualitative and achieves only moderate agreement between radiologists. We have developed an automated, quantitative algorithm that generates a local detectability (d L) map, which estimates the likelihood that a simulated lesion would be missed if present. The d L map is computed by tessellating the mammogram into overlapping regions of interest, for which the detectability of a simulated lesion by a non-prewhitening model observer is calculated using local estimates of the noise power spectrum and volumetric breast density. The algorithm considers both the effects of loss of contrast due to density and the distracting appearance of density on lesion conspicuity.
In previous work, it has been shown that the mean d L from the maps are strongly correlated to detection performance by computerized and human readers in a controlled reader study. Here, we investigate how various statistical features of the d L maps (gray-level histogram and co-occurrence features) are related to the diagnostic performance of mammography in a set of images comprised of 8 cancer cases that were mammographically occult and 40 cancer that were detected in screening mammography.
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This project has been supported financially by research grants from The Ontario Institute for Cancer Research.
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Alonzo-Proulx, O., Mainprize, J., Hussein, H., Jong, R., Yaffe, M. (2016). Local Detectability Maps as a Tool for Predicting Masking Probability and Mammographic Performance. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_29
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DOI: https://doi.org/10.1007/978-3-319-41546-8_29
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