TY - GEN
T1 - A computer-aided detection system for automatic mammography mass identification
AU - Samma, Hussein
AU - Lim, Chee Peng
AU - Samma, Ali
PY - 2010
Y1 - 2010
N2 - Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.
AB - Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.
UR - https://www.scopus.com/pages/publications/78650187790
U2 - 10.1007/978-3-642-17534-3_28
DO - 10.1007/978-3-642-17534-3_28
M3 - Conference contribution
AN - SCOPUS:78650187790
SN - 3642175333
SN - 9783642175336
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 226
EP - 233
BT - Neural Information Processing
T2 - 17th International Conference on Neural Information Processing, ICONIP 2010
Y2 - 22 November 2010 through 25 November 2010
ER -
