Estimation of breast density: an adaptive moment preserving method for segmentation of fibroglandular tissue in breast magnetic resonance images.

Purpose: Breast density has been found to be a potential indicator for breast cancer risk. The estimation of breast density can be seen as a segmentation problem on fibroglandular tissues from a breast magnetic resonance image. The classic moment preserving is a thresholding method, which can be applied to determine an appropriate threshold value for fibroglandular tissue segmentation.

Methods: This study proposed an adaptive moment preserving method, which combines the classic moment preserving and a thresholding adjustment method. The breast MR images are firstly performed to extract the fibroglandular tissue from the breast tissue. The next step is to obtain the areas of the fibroglandular tissue and the whole breast tissue. Finally, breast density can be estimated for the given breast.

Results: The Friedman test shows that the qualities of segmentation are insignificant with p<0.000 and Friedman chi-squared=1116.12. The Friedman test shows that there would be significant differences in the sum of the ranks of at least one segmentation method. Average ranks indicate that the performance of the four methods is ranked as adaptive moment preserving, fuzzy c-means, moment preserving, and Kapur's method in order. Among the four methods, adaptive moment preserving also achieves the minimum values of MAE and RMSE with 9.2 and 12.

Conclusion: This study has verified that the proposed adaptive moment preserving can identify and segment the fibroglandular tissues from the 2D breast MR images and estimate the degrees of breast density.