For the radiologists, it is difficult to identify the mass on a mammo-gram since the masses are surrounded by the mammary gland and blood vessel. In current breast cancer screening, about 10%-30% of tumors are often missed by radiologists owing to the ambiguous margins of lesions and the visual fatigue of radiologists resulting from the long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiolo-gists in detecting mammographic lesions that may indicate the presence of breast cancer. The purpose of this study is to construct an automated CADe system us-ing a new feature extraction method for mammographic mass detection. In this system, some adaptive square regions of interest (ROIs) are segmented according to the size of suspicious areas. Then a new feature extraction method adopting grey level co-occurrence matrix and optical density features called GLCM-OD features is applied to each ROI. The GLCM-OD features describe local grey level texture characteristics and the whole photometric distribution of the ROI. Finally, the stepwise linear discriminant analysis is applied to classify abnormal regions by selecting and rating individual performance of each feature. This sys-tem is trained and tested by 358 mammographic cases from the digital database of screening mammography (DDSM). The proposed system averagely provides sensitivityof 97.3% with4.9falsepositives per image andtheAzis0.981. There-sults prove that the proposed system achieves satisfactory detection performance.