Abstract

The goal of the proposed research is to develop computer-aided diagnosis (CAD) schemes to assist radiologists in diagnosing breast cancer from mammograms. We believe that CAD will improve radiologists' ability to interpret mammograms, so that both the number of missed cancers and the number of women unnecessarily sent to biopsy can be reduced. This proposal is specifically for the development of CAD schemes for the detection and the classification (benign versus malignant) of microcalcifications from digital mammograms. Under existing NIH funding, other research projects in our laboratory are directed at developing computer schemes for the detection and classification of breast masses. The schemes for both masses and microcalcifications will be tested together in the final years of this proposal.
The specific aims are: (1) To establish a large database of normal and abnormal (benign and malignant) mammograms, consisting of 2400 digitized screen-film mammograms (600 cases). (2) To develop an improved computer scheme for the automated detection of clustered microcalcifications by developing nonlinear filters, based on morphological operators, and advanced feature-extraction techniques, based on the radiographic features of microcalcifications and on the physical characteristics of the image receptor. Artificial neural networks will also be used to eliminate false-positive clusters detected by the computer scheme. (3) To develop a computer scheme for the classification of clustered microcalcifications. Two different approaches will be used -- artificial neural networks and rule-based techniques. Computer techniques to extract radiographic features of microcalcifications will be developed and these features will be used as input to the two classifiers. 4) To evaluate the ability of CAD to improve radiologists' accuracy in detecting and classifying breast lesions using ROC analysis. In addition, a pilot study for the clinical evaluation of the CAD schemes will be conducted.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA060187-01A1
Application #
2100875
Study Section
Diagnostic Radiology Study Section (RNM)
Project Start
1994-01-01
Project End
1997-12-31
Budget Start
1994-01-01
Budget End
1994-12-31
Support Year
1
Fiscal Year
1994
Total Cost
Indirect Cost

  Institution

Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
225410919
City
Chicago
State
IL
Country
United States
Zip Code
60637

  Publications

Jiang, Yulei; Nishikawa, Robert M; Schmidt, Robert A et al. (2006) Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. Acad Radiol 13:84-94
Edwards, Darrin C; Metz, Charles E; Nishikawa, Robert M (2005) The hypervolume under the ROC hypersurface of ""near-guessing"" and ""near-perfect"" observers in N-class classification tasks. IEEE Trans Med Imaging 24:293-9
Edwards, Darrin C; Lan, Li; Metz, Charles E et al. (2004) Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions. Med Phys 31:81-90
Salfity, Maria F; Nishikawa, Robert M; Jiang, Yulei et al. (2003) The use of a priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys 30:823-31
Edwards, Darrin C; Kupinski, Matthew A; Metz, Charles E et al. (2002) Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys 29:2861-70
El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N et al. (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21:1552-63
Jiang, Y; Nishikawa, R M; Schmidt, R A et al. (2001) Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. Radiology 220:787-94
Jiang, Y; Nishikawa, R M; Papaioannou, J (2001) Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications. Med Phys 28:1949-57
Jiang, Y; Nishikawa, R M; Schmidt, R A et al. (1999) Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol 6:22-33
Doi, K; MacMahon, H; Katsuragawa, S et al. (1999) Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol 31:97-109

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