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Proceedings Paper

Digital breast tomosynthesis: computerized detection of microcalcifications in reconstructed breast volume using a 3D approach
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Paper Abstract

We are developing a computer-aided detection (CAD) system for clustered microcalcifications in digital breast tomosynthesis (DBT). In this preliminary study, we investigated the approach of detecting microcalcifications in the tomosynthesized volume. The DBT volume is first enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices with a calcification response function and signal-to-noise ratio enhancement filtering. Potential signal sites are identified in the enhanced volume and local analysis is performed to further characterize each object. A 3D dynamic clustering procedure is designed to locate potential clusters using hierarchical criteria. We collected a pilot data set of two-view DBT mammograms of 39 breasts containing microcalcification clusters (17 malignant, 22 benign) with IRB approval. A total of 74 clusters were identified by an experienced radiologist in the 78 DBT views. Our prototype CAD system achieved view-based sensitivity of 90% and 80% at an average FP rate of 7.3 and 2.0 clusters per volume, respectively. At the same levels of case-based sensitivity, the FP rates were 3.6 and 1.3 clusters per volume, respectively. For the subset of malignant clusters, the view-based detection sensitivity was 94% and 82% at an average FP rate of 6.0 and 1.5 FP clusters per volume, respectively. At the same levels of case-based sensitivity, the FP rates were 1.2 and 0.9 clusters per volume, respectively. This study demonstrated that computerized microcalcification detection in 3D is a promising approach to the development of a CAD system for DBT. Study is underway to further improve the computer-vision methods and to optimize the processing parameters using a larger data set.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241D (9 March 2010); doi: 10.1117/12.844511
Show Author Affiliations
Heang-Ping Chan, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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