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Journal Article
Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis
- Authors
- Ji-wook Jeong, Seung-Hoon Chae, Eun Young Chae, Hak Hee Kim, Young-Wook Choi, Sooyeul Lee
- Issue Date
- 2016-03
- Citation
- BioMed Research International, v.2016, pp.1-9
- ISSN
- 2314-6133
- Publisher
- Hindawi Publishing
- Language
- English
- Type
- Journal Article
- 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%.
- KSP Keywords
- Bounding Box, Clustering centers, Computer-aided Detection(CADe), Detection of microcalcification, Digital breast tomosynthesis(DBT), Enhanced image, FP reduction, MC detection, Microcalcification clusters(MCC), Multiscale hessian-based, Response Function
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
