Fully-automated fibroglandular tissue segmentation in breast MRI

Abstract

We propose an automated segmentation method for estimating the fibroglandular (i.e., dense) tissue in breast MRI. The first step of our method is to segment the breast as an organ from other imaged parts through an integrated edge extraction and voting algorithm. Then, we apply the nonparametric non-uniform intensity normalization (N3) algorithm to the segmented breast to correct bias field which is common in breast MRI. After that, fuzzy C-means clustering is performed to categorize the breast tissue into two clusters, i.e., fibroglandular tissue and fat. The automated segmentation results are compared to manual segmentations, verified by an experienced breast imaging radiologist, to assess the accuracy of the algorithm, where the Dice's Similarity Coefficient (DSC) shows a 0.73 agreement in our experiments. The benefit of the bias correction step is also shown through the comparison with the results obtained by excluding the bias correction step. © 2012 Springer-Verlag Berlin Heidelberg.