A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI
A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI
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DOI：
10.1118/1.3519869
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发表时间：
2011-01-01
期刊：
影响因子：
3.8
通讯作者：
Su, Min-Ying
中科院分区：
文献类型：
--
作者：
Lin, Muqing;Chan, Siwa;Su, Min-Ying
目的：定量乳腺密度被认为是与乳腺癌发展相关的一个强有力的危险因素。基于三维乳房MRI的乳腺密度测量可以提供非常有用的信息。MRI定量分析乳腺密度的一个重要步骤是校正场的不均匀性，以便准确分割纤维腺组织（致密组织）。将非参数非均匀性归一化（N3）算法与基于模糊c均值（FCM）的非均匀性校正算法相结合，提出了一种新的偏置场校正方法。方法：对使用1.5 T MRI扫描仪获得的非脂肪sat t1加权图像进行分析。研究人员对30名健康志愿者的60个乳房进行了分析。N3被认为是一种鲁棒校正方法，但它不能对大面积的强偏置场进行校正。基于fcm的算法可以在较大范围内校正偏场，但会改变组织对比度，影响分割质量。该算法首先使用N3，然后使用FCM，然后使用高斯核和b样条曲面拟合对生成的偏置场进行平滑，以最大限度地减少误改变组织对比度的问题。将N3 + FCM校正后的图像分割结果与N3和FCM单独校正后的图像和另一种方法相干局部强度聚类（CLIC）校正后的图像分割结果进行比较。由放射科医师对不同校正方法的分割质量进行评价和排序。结果：作者证明了迭代N3 + FCM校正方法使脂肪组织的信号强度变亮，并将纤维腺组织和脂肪组织之间的直方图峰分离开来，从而实现了纤维腺组织和脂肪组织之间的准确分割。在第一次阅读中，放射科医生发现17个乳房排名（N3 + FCM > N3 > FCM）， 7个乳房排名（N3 + FCM = N3 = FCM）， 32个乳房排名（N3 + FCM = N3 > FCM）， 2个乳房排名（N3 + FCM = N3 = FCM）， 2个乳房排名（N3 > N3 + FCM > FCM）。第二次阅读的结果也差不多。两两Wilcoxon sign -rank检验均显著，N3 + FCM优于N3和FCM， N3优于FCM。新的N3 + FCM算法的性能与CLIC相当，在57/60个乳房中显示相同的质量。结论：选择合适的偏场校正方法是基于乳腺MRI准确分割纤维腺组织定量测量乳腺密度的一个非常重要的预处理步骤。结合N3 + FCM和CLIC的算法均取得了满意的结果。(C) 2011年美国医学物理学家协会。(DOI: 10.1118/1.3519869)
Purpose: 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 tissue contrast 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 tissue contrast. The segmentation results based on the N3 + FCM 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 N3 + FCM 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 (N3 + FCM > N3 > FCM) ranking in 17 breasts, (N3 + FCM = N3 = FCM) ranking in 7 breasts, (N3 + FCM = N3 > FCM) in 32 breasts, (N3 + FCM = N3 = FCM) in 2 breasts, and (N3 > N3 + FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3 + FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3 + FCM 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 N3 + FCM and CLIC both yield satisfactory results. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3519869]