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Sensors 2013, 13(4), 4855-4875; doi:10.3390/s130404855
Article

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

1
,
2
,
3,* , 3,* , 1
,
1
 and
1
1 Department of Radiology, China Medical University Hospital, Taichung 404, Taiwan 2 Department of Electrical Engineering, National Chia-Yi University, Chiayi 600, Taiwan 3 Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan
* Authors to whom correspondence should be addressed.
Received: 16 February 2013 / Revised: 18 March 2013 / Accepted: 2 April 2013 / Published: 11 April 2013
(This article belongs to the Section Physical Sensors)
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Abstract

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.
Keywords: mammography; clustered microcalcification; texture features; support vector machines mammography; clustered microcalcification; texture features; support vector machines
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Huang, Y.-J.; Chan, D.-Y.; Cheng, D.-C.; Ho, Y.-J.; Tsai, P.-P.; Shen, W.-C.; Chen, R.-F. Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection. Sensors 2013, 13, 4855-4875.

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