  Breast cancer is one of the most serious disease affecting women's health.
Due to low cost, portable, no radiation, and high efficiency, breast ultrasound
(BUS) imaging is the most popular approach for diagnosing early breast cancer.
However, ultrasound images are low resolution and poor quality. Thus,
developing accurate detection system is a challenging task. In this paper, we
propose a fully automatic segmentation algorithm consisting of two parts: fuzzy
fully convolutional network and accurately fine-tuning post-processing based on
breast anatomy constraints. In the first part, the image is preprocessed by
contrast enhancement, and wavelet features are employed for image augmentation.
A fuzzy membership function transforms the augmented BUS images into fuzzy
domain. The features from convolutional layers are processed using fuzzy logic
as well. The conditional random fields (CRFs) post-process the segmentation
result. The location relation among the breast anatomy layers is utilized to
improve the performance. The proposed method is applied to the dataset with 325
BUS images, and achieves state-of-art performance compared with that of
existing methods with true positive rate 90.33%, false positive rate 9.00%, and
intersection over union (IoU) 81.29% on tumor category, and overall
intersection over union (mIoU) 80.47% over five categories: fat layer, mammary
layer, muscle layer, background, and tumor.
