Over the past 15 years, considerable progress has been made in the development of computer-aided detection (CAD) of abnormalities in mammograms. Nevertheless, because of performance limitations of current CAD algorithms, the question of whether CAD provides a net benefit remains unresolved. In recent years, despite considerable effort by many groups, the rate of improvement in CAD performance has declined to the point that performance statistics seem to be approaching an asymptote, which is well below the performance of mammographers. The most likely reason for this is that essentially all current CAD implementations are founded on traditional methods of signal processing and pattern recognition and derive their performance by detecting features in a single image. These features are then classified by some inference mechanism. It is conceivable (probable) that most of the relevant physical features in single images have been identified and exploited to some extent. The hypothesis of this proposal is that performance limitations of current CAD, as indicated by the difference in performance between CAD and mammographers, result to a large extent from the failure of these algorithms to utilize data that can only be derived by a synergistic analysis of multiple images. Thus, it is the intent of this proposal to extend current CAD methodology to enable the extraction of information related to the spatial structure of a breast from ipsilateral views. Our preliminary results have established that despite compression-induced distortion, there are features that can be derived automatically from pairs of images and have been shown to provide information not obtainable from the independent analysis of single images. These multi-image-based features are partially independent of tissue distortion from breast compression during mammography. We will investigate and refine these and other features that can be identified. The purpose of this investigation is to fully exploit these kinds of features and optimize their contributions to a multi-image-based CAD algorithm.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA080836-03
Application #
6497530
Study Section
Special Emphasis Panel (ZRG1-DMG (04))
Program Officer
Croft, Barbara
Project Start
2000-02-01
Project End
2004-01-31
Budget Start
2002-02-01
Budget End
2003-01-31
Support Year
3
Fiscal Year
2002
Total Cost
$279,510
Indirect Cost
Name
University of Pittsburgh
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
053785812
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Wang, Xiao Hui; Durick, Janet E; Lu, Amy et al. (2008) Characterization of radiologists'search strategies for lung nodule detection: slice-based versus volumetric displays. J Digit Imaging 21 Suppl 1:S39-49
Zheng, Bin; Leader, Joseph K; Abrams, Gordon et al. (2004) Computer-aided detection schemes: the effect of limiting the number of cued regions in each case. AJR Am J Roentgenol 182:579-83
Zheng, Bin; Swensson, Richard G; Golla, Sara et al. (2004) Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments. Acad Radiol 11:398-406
Zheng, Bin; Good, Walter F; Armfield, Derek R et al. (2003) Performance change of mammographic CAD schemes optimized with most-recent and prior image databases. Acad Radiol 10:283-8
Zheng, Bin; Hardesty, Lara A; Poller, William R et al. (2003) Mammography with computer-aided detection: reproducibility assessment initial experience. Radiology 228:58-62
Chang, Yuan-Hsiang; Good, Walter F; Leader, Joseph K et al. (2003) Integrated density of a lesion: a quantitative, mammographically derived, invariable measure. Med Phys 30:1805-11
Zheng, Bin; Shah, Ratan; Wallace, Luisa et al. (2002) Computer-aided detection in mammography: an assessment of performance on current and prior images. Acad Radiol 9:1245-50