Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture

Abstract

Mammogram is an X-ray image of the breast. It plays an important role in the breast cancer early diagnosis. In recent years, computer aided detection (CAD) is used for breast cancer detection. Multi-view of mammograms are needed to achieve high accuracy of automatic detection. Since nipple is the only landmark on mammogram of different views (mediolateral oblique (MLO) and craniocaudal (CC) views), nipple detection becomes the first important step of many CAD systems. Researchers have developed different models to detect nipple in recent 20 years. Grey scale, geometric feature and breast edge's gradient are used to find the nipple on the mammogram. For most methods, MLO and CC views need to be tested separately, and obvious and subtle types of nipples also need different methods to detect. In this paper, a model with deep learning is designed to locate nipples on mammogram of both MLO and CC views. Both obvious and subtle types are used for experiment. Four convolutional neural network blocks are used to attain candidate blocks. Normalization layers are added to the proposed model in order to improve the domain adaptation. Based on the intersection of candidates, the model computes the final block of nipple. In this experiment, train set and test set are randomly attained from Digital Database for Screening Mammography (DDSM). Our proposed method achieved an overall nipple detection accuracy of 98.00%, which outperformed three comparative methods.