Next Article in Journal
Previous Article in Journal
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
Received: 16 February 2013; in revised form: 18 March 2013 / Accepted: 2 April 2013 / Published: 11 April 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1458 KB, uploaded 21 June 2014]
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

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.

AMA Style

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(4):4855-4875.

Chicago/Turabian Style

Huang, Yi-Jhe; Chan, Ding-Yuan; Cheng, Da-Chuan; Ho, Yung-Jen; Tsai, Po-Pang; Shen, Wu-Chung; Chen, Rui-Fen. 2013. "Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection." Sensors 13, no. 4: 4855-4875.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert