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Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: A preliminary study
Author(s) -
Reiser I.,
Nishikawa R. M.,
Edwards A. V.,
Kopans D. B.,
Schmidt R. A.,
Papaioannou J.,
Moore R. H.
Publication year - 2008
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2885366
Subject(s) - microcalcification , computer science , artificial intelligence , mammography , digital breast tomosynthesis , digital mammography , computer vision , projection (relational algebra) , image quality , tomosynthesis , breast imaging , pattern recognition (psychology) , algorithm , breast cancer , image (mathematics) , medicine , cancer
Digital breast tomosynthesis (DBT) is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters (MCCs) for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 at 1.3 false positive clusters, which is below that of current MCC detection algorithms for full‐field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal‐to‐noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.