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Proceedings Paper

Detection of microcalcifications in breast tomosynthesis reconstructed with multiscale bilateral filtering regularization
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Paper Abstract

We are developing a CAD system to assist radiologists in detecting microcalcification clusters (MCs) in digital breast tomosynthesis (DBT). In this study, we investigated the feasibility of using as input to the CAD system an enhanced DBT volume that was reconstructed with the iterative simultaneous algebraic reconstruction technique (SART) regularized by a new multiscale bilateral filtering (MBiF) method. The MBiF method utilizes the multiscale structures of the breast to selectively enhance MCs and preserve mass spiculations while smoothing noise in the DBT images. The CAD system first extracted the enhancement-modulated calcification response (EMCR) in the DBT volume. Detection of the seed points for MCs and individual calcifications were guided by the EMCR. MC candidates were formed by dynamic clustering. FPs were further reduced by analysis of the feature characteristics of the MCs. With IRB approval, two-view DBT of 91 subjects with biopsy-proven MCs were collected. Seventy-eight views from 39 subjects with MCs were used for training and the remaining 52 cases were used for independent testing. For view-based detection, a sensitivity of 85% was achieved at 3.23 FPs/volume. For case-based detection, the same sensitivity was obtained at 1.63 FPs/volume. The results indicate that the new MBiF method is useful in improving the detection accuracy of clustered microcalcifications. An effective CAD system for microcalcification detection in DBT has the potential to eliminate the need for additional mammograms, thereby reducing patient dose and reading time.

Paper Details

Date Published: 18 March 2013
PDF: 8 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701L (18 March 2013); doi: 10.1117/12.2008230
Show Author Affiliations
Ravi K. Samala, Univ. of Michigan, Ann Arbor (United States)
Heang-Ping Chan, Univ. of Michigan, Ann Arbor (United States)
Yao Lu, Univ. of Michigan, Ann Arbor (United States)
Lubomir Hadjiyski, Univ. of Michigan, Ann Arbor (United States)
Jun Wei, Univ. of Michigan, Ann Arbor (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Mark Helvie, Univ. of Michigan, Ann Arbor (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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