Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection

作者：Huang Yi Jhe; Chan Ding Yuan; Cheng Da Chuan^*; Ho Yung Jen; Tsai Po Pang; Shen Wu Chung; Chen Rui Fen

来源：Sensors, 2013, 13(4): 4855-4875.

DOI：10.3390/s130404855

• mammography
• clustered microcalcification
• texture features
• support vector machines

We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatically, regardless of the input training mammograms used. We tested the proposed scheme using a database of 111 clinical mammograms containing 1,050 microcalcifications (MCs). The accuracy of the system was examined via a free response receiver operating characteristic (fROC) curve of the test dataset. The system performance for MC identifications was Az = 0.9897, the sensitivity was 92%, and 0.65 false positives (FPs) were generated per image for MCC detection.

• 出版日期2013-4
• 单位中国医科大学