9 March 2010 Robustness of interactive intensity thresholding based breast density assessment in MR-mammography
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Abstract
The efficiency of breast density assessment using interactive intensity thresholding applied to intensity uniformity corrected T1-weighted MR images is investigated for 20 healthy women who attended the UK multi-centre study of MRI screening for breast cancer. Mammographic density is estimated on the medial-lateral oblique X-ray mammograms using CUMULUS. MR density assessment is performed using both high and low-resolution T1-weighted images. The left and the right breast regions anterior to the pectoral muscle were segmented on these images using active contouring. For each region, intensity uniformities were corrected using proton density images and a user selected uniformity factor. An interactively selected threshold is applied to the corrected images to detect fibrogulandular tissue. The breast density is calculated as the ratio of the classified fibroglandular tissue to the segmented breast volume. There is no systematic difference, good consistency and a high correlation between the left and the right breast densities estimated from X-ray mammograms and the high and low-resolution MR images. The correlation is the highest and the consistency is the best for the low-resolution MR measurements (r=0.976, MeanAbsoluteDifference = 2.12%). Mean breast densities calculated over the left and the right breasts on high and low-resolution MR images are highly correlated with mammographic density (r=0.923 and 0.903, respectively) but are approximately 50% lower. Interactive intensity thresholding of T1-weighted MR images provides an easy, reproducible and reliable way to assess breast density. High and low-resolution measurements are both highly correlated with the mammographic density but the latter requires less processing and acquisition time.
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Sa. Reed, G. Ertas, S. Doran, R. M. Warren, M. O. Leach, "Robustness of interactive intensity thresholding based breast density assessment in MR-mammography", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242A (9 March 2010); doi: 10.1117/12.844337; https://doi.org/10.1117/12.844337
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