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BioMed Research International
Volume 2016 (2016), Article ID 8651573, 8 pages
http://dx.doi.org/10.1155/2016/8651573
Research Article

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

1SW·Content Research Laboratory, Electronics & Telecommunications Research Institute, 218 Gajeongno, Yuseong-gu, 34129 Daejeon, Republic of Korea
2Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505 Seoul, Republic of Korea
3Advanced Medical Device Research Division, Korea Electrotechnology Research Institute, 111 Hanggaul-ro, Sangnok-gu, Ansan-si, 15588 Gyeonggi-do, Republic of Korea

Received 10 September 2015; Revised 12 February 2016; Accepted 17 February 2016

Academic Editor: Guang Jia

Copyright © 2016 Ji-wook Jeong et al. 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.

Abstract

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.