| Popis: |
Breast density is now recognized as one of the most important independent risk factors of breast cancer. Current means to assess breast density primarily utilize mammograms which represent a series of projection images, making it difficult to estimate the true volume of the fibroglandular tissue. We present 3D transmission ultrasound as a method to visualize and differentiate fibroglandular tissue within the breast and use an unsupervised learning-based method to quantitatively assess the respective breast density. The method includes initial separation of breast from the surrounding water bath followed by segmentation of the whole breast into fibroglandular tissue and fat using fuzzy C-mean (FCM) classification. We apply these methods to both tissue phantoms (in vitro) and clinical breast images (in vivo). In the case of tissue phantoms, the agreement between the theoretical (geometric density) and experimentally calculated values was better than 90%. For density calculation in a sample size of 50 cases, the results correlate well (Spearman r = 0.93, 95% CI: 0.88-0.96, p |
