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. 2008 Apr;35(4):1486-93.
doi: 10.1118/1.2885366.

Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study

I Reiser  1 , R M Nishikawa, A V Edwards, D B Kopans, R A Schmidt, J Papaioannou, R H Moore
Affiliations

Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study

I Reiser et al. Med Phys. 2008 Apr.

Abstract

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.

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Figures

Figure 1
Figure 1
Distribution of number of microcalcifications per cluster in the database. The mean number of microcalcifications per cluster is 7.3.
Figure 2
Figure 2
Flow chart of the proposed MCC detection scheme.
Figure 3
Figure 3
Microcalcification cluster in the ML-EM reconstructed slice (top left), and in the denoised log projections.
Figure 4
Figure 4
(a): Log-projection image of a MCC. (b): Denoised log-projection image of the same cluster. This MCC is the same as the 25° projection in Fig. 3.
Figure 5
Figure 5
Example of (a) a slice of the point backprojection volume containing the MCC, and (b) the corresponding MIP image.
Figure 6
Figure 6
FROC curve after point backprojection: True-positive-cluster fraction as a function of the average number of false-positive clusters per volume. Performance was estimated through self-consistency.
Figure 7
Figure 7
Algorithm performance including feature analysis and clustering stages, estimated using round-robin analysis.
See this image and copyright information in PMC

References

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