Towards Accurate Segmentation of Fibroglandular Tissue in Breast MRI Using Fuzzy C-Means and Skin-Folds Removal

Breast density measuring the volumetric portion of fibroglandular tissue is considered as an important factor in evaluating breast cancer risk of women. Categorizing breast density into different levels by human observers is time-consuming and subjective, which may result in large inter-reader variability. In this work, we propose a fully automated fibroglandular tissue segmentation technique aiming to assist automatic breast density measurement in magnetic resonance imaging (MRI). Firstly, a bias field correction algorithm is applied. Secondly, the breast mask is segmented to exclude air background and thoracic tissues, such as liver, heart and lung. Thirdly, the segmentation is further refined by removing the skin-folds that are normally included in the breast mask and mimic the fibroglandular tissue, leading to incorrect density estimation. Finally, we apply a fuzzy c-means approach to extract the fibroglandular tissue within the breast mask. To quantitatively evaluate the proposed method, a total of 50 MR scans were collected. By comparing the volume overlap between manually annotated fibroglandular tissue with the results of our method, we achieved an average Dice Similarity Coefficient (DSC) of 0.84.