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Proceedings Paper

Digital tomosynthesis mammography: comparison of mass classification using 3D slices and 2D projection views
Author(s): Heang-Ping Chan; Yi-Ta Wu; Berkman Sahiner; Yiheng Zhang; Jun Wei; Richard H. Moore; Daniel B. Kopans; Mark A. Helvie; Lubomir Hadjiiski; Ted Way
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Paper Abstract

We are developing computer-aided diagnosis (CADx) methods for classification of masses on digital breast tomosynthesis mammograms (DBTs). A DBT data set containing 107 masses (56 malignant and 51 benign) collected at the Massachusetts General Hospital was used. The DBTs were obtained with a GE prototype system which acquired 11 projection views (PVs) over a 50-degree arc. We reconstructed the DBTs at 1-mm slice interval using a simultaneous algebraic reconstruction technique. The regions of interest (ROIs) containing the masses in the DBT volume and the corresponding ROIs on the PVs were identified. The mass on each slice or each PV was segmented by an active contour model. Spiculation measures, texture features, and morphological features were extracted from the segmented mass. Four feature spaces were formed: (1) features from the central DBT slice, (2) average features from 5 DBT slices centered at the central slice, (3) features from the central PV, and (4) average features from all 11 PVs. In each feature space, a linear discriminant analysis classifier with stepwise feature selection was trained and tested using a two loop leave-one-case-out procedure. The test Az of 0.91±0.03 from the 5-DBT-slice feature space was significantly (p=0.003) higher than that of 0.84±0.04 from the 1-DBT-slice feature space. The test Az of 0.83±0.04 from the 11-PV feature space was not significantly different (p=0.18) from that of 0.79±0.04 from the 1-PV feature space. The classification accuracy in the 5-DBT-slice feature space was significantly better (p=0.006) than that in the 11-PV feature space. The results demonstrate that the features of breast lesions extracted from the DBT slices may provide higher classification accuracy than those from the PV images.

Paper Details

Date Published: 17 March 2008
PDF: 6 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691506 (17 March 2008); doi: 10.1117/12.770422
Show Author Affiliations
Heang-Ping Chan, Univ. of Michigan (United States)
Yi-Ta Wu, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Yiheng Zhang, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Richard H. Moore, Massachusetts General Hospital (United States)
Daniel B. Kopans, Massachusetts General Hospital (United States)
Mark A. Helvie, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Ted Way, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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