Quantitative breast density is known as a strong risk factor associated with the development of breast cancer.Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work.Methods:
The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissuecontrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissuecontrast. The segmentation results based on the corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked.Results:
The authors demonstrated that the iterative correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found ranking in 17 breasts, ranking in 7 breasts, in 32 breasts, in 2 breasts, and in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing superior to both N3 and FCM, and N3 superior to FCM. The performance of the new algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts.Conclusions:
Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining and CLIC both yield satisfactory results.
This work was supported, in part, by the NIH (Grant Nos. R01 CA127927 and R03 CA136071), by the California BCRP (Grant Nos. 14GB-0148 and 16GB-0056), and by the National Science Council of Taiwan (Grant No. NSC-98-2221-E-039-009).
II. MATERIALS AND METHODS
II.B. Breast segmentation and inhomogeneity correction
II.C. Fibroglandular tissue segmentation
II.D. Evaluation of segmentation quality by four methods
II.E. Statistical analysis
III.A. Comparison of inhomogeneity correction
III.B. Segmentation quality based on compared to using N3 and FCM alone
III.C. Comparison of segmentation quality based on and CLIC
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