TY - GEN
T1 - Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture
AU - Lin, Yuyang
AU - Li, Muyang
AU - Chen, Sirui
AU - Yu, Limin
AU - Ma, Fei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Deep learning
KW - Mammogram
KW - Nipple detection
UR - http://www.scopus.com/inward/record.url?scp=85079167762&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI48845.2019.8966022
DO - 10.1109/CISP-BMEI48845.2019.8966022
M3 - Conference Proceeding
AN - SCOPUS:85079167762
T3 - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
BT - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
A2 - Li, Qingli
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Y2 - 19 October 2019 through 21 October 2019
ER -