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Computerized mass detection for digital breast tomosynthesis directly from the projection images
Author(s) -
Reiser I.,
Nishikawa R. M.,
Giger M. L.,
Wu T.,
Rafferty E. A.,
Moore R.,
Kopans D. B.
Publication year - 2006
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 - 10.1118/1.2163390
Subject(s) - artificial intelligence , projection (relational algebra) , mammography , computer science , voxel , tomosynthesis , linear discriminant analysis , modality (human–computer interaction) , computer vision , breast imaging , false positive paradox , medical imaging , volume (thermodynamics) , feature (linguistics) , digital breast tomosynthesis , iterative reconstruction , pattern recognition (psychology) , nuclear medicine , algorithm , medicine , physics , breast cancer , linguistics , philosophy , cancer , quantum mechanics
Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three‐dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees . Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen‐film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three‐dimensional (3‐D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.