Share Email Print

Proceedings Paper

Influence of signal-to-noise ratio and temporal stability on computer-aided detection of mammographic microcalcifications in digitized screen-film and full-field digital mammography
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Most computer-aided detection (CADe) schemes were developed for digitized screen-film mammography (dSFM) and are being transitioned to full-field digital mammography (FFDM). In this research, phantoms were used to relate image quality differences to the performance of the multiple components of our microcalcification CADe scheme, and to identify to what extent, if any, each CADe component is likely to require modification for FFDM. We compared multiple image quality metrics for a dSFM imaging chain (GE DMR, MinR-2000 and Lumisiys digitizer) and an FFDM system (GE Senographe 2000D) and related them to CADe performance for images of 1) contrast-detail phantom disks and 2) microcalcification phantoms (bone fragments and cadaver breasts). Higher object signal-to noise ratio (SNR) in FFDM compared with dSFM (p<0.05 for 62% of disks, and p>0.05 for 32% of disks) led to superior CADe signal and cluster detection FROC performance. Signal segmentation was comparable (p>0.05 for 74% of disks) in dSFM and FFDM and superior in FFDM (p<0.05) for 19% of disks. Better FFDM temporal stability led to more reproducible CADe performance. For microcalcification phantoms, seven of eight computer-calculated features performed better or comparably (p<0.05) at classifying true- and false-positive detections in FFDM. In conclusion, the image quality improvements offered by FFDM compared to dSFM led to comparable or improved performance of the multiple stages of our CADe scheme for microcalcification detection.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69151X (17 March 2008); doi: 10.1117/12.773069
Show Author Affiliations
Laura M. Yarusso, The Univ. of Chicago (United States)
Robert M. Nishikawa, The Univ. of Chicago (United States)

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

© SPIE. Terms of Use
Back to Top