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

Sensors

Yi-Jhe Huang²  Ding-Yuan Chan³  Da-Chuan Cheng¹  Yung-Jen Ho¹  Po-Pang Tsai²  Wu-Chung Shen² 

[1] Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan;Department of Radiology, China Medical University Hospital, Taichung 404, Taiwan; E-Mails:;Department of Electrical Engineering, National Chia-Yi University, Chiayi 600, Taiwan; E-Mail:

关键词: mammography;    clustered microcalcification;    texture features;    support vector machines;   

DOI  :  10.3390/s130404855

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.