Three-class ROC analysis - A decision theoretic approach under the ideal observer framework

IEEE Transactions on Medical Imaging

Volumn 25, Issue 5, 2006, Pages 571-581

  He, Xin ^a,b   Metz, Charles E ^c   Tsui, Benjamin M W ^a,b   Links, Jonathan M ^d   Frey, Eric C ^a,b  

^a IEEE   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

^d The Johns Hopkins University   (United States)

Author keywords

Ideal observers; ROC analysis; Three class classification

Indexed keywords

IDEAL OBSERVERS; ROC ANALYSIS; THREE-CLASS CLASSIFICATION;

CONSTRAINT THEORY; DATA ACQUISITION; DIAGNOSIS; IMAGE PROCESSING; IMAGE RECONSTRUCTION; MAXIMUM LIKELIHOOD ESTIMATION;

DECISION THEORY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; OBSERVER VARIATION; ROC CURVE; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION SUPPORT TECHNIQUES; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; OBSERVER VARIATION; ROC CURVE;

EID: 33646408113     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2006.871416     Document Type: Article

References (14)

1. H. H. Barrett and K. J. Myers, Foundations of Image Science. New York: Wiley, 2003.
2. C. E. Metz, "Basic principles of ROC analysis," Sem. Nucl. Med., vol. 8, pp. 283-298, 1978.
3. _, "ROC methodology in radiologic imaging," Invest. Radiol., vol. 21, pp. 720-733, 1986.
4. M. R. Gold, J. E. Siegel, L. B. Russell, and M. C. Weinstein, Cost-effectiveness in Health and Medicine, Oxford, U.K.: Oxford Univ. Press, 1996.
5. D. Mossman, "Three-way ROCs," Med. Decision Making, vol. 19, pp. 78-89, 1999.
6. D. C. Edwards and C. E. Metz, "Analysis of proposed three-class classification decision rules in terms of the ideal observer decision rule," J. Math. Psychol., 2005, submitted for publication.
7. B. K. Scurfield, "Generalization of the theory of signal detectability to n-event m-dimensional forced-choice tasks," J. Math. Psychol., vol. 42, pp. 5-31, 1998.
8. H. L. Van Trees, Detection, Estimation and Modulation Theory: Part I. New York: Wiley, 1968.
9. C. E. Metz, "The optimal decision variable: likelihood ratio," in Mathematics for Medical Physicists, Univ. Chicago Course Handout, 2000.
10. D. C. Edwards, C. E. Metz, and M. A. Kupinski, "Ideal observers and optimal ROC hypersurfaces in N-class classification," IEEE Trans. Med. Imag., vol. 23, no. 7, pp. 891-895, Jul. 2004.
11. _, "The hypervolume under the ROC hypersurface of "near-guessing" and "near-perfect" observers in N-class classification tasks," IEEE Trans. Med. Imag., vol. 24, no. 3, pp. 293-299, Mar 2005.
12. D.C.Edwards, Ideal Observer Estimation and Generalised ROC Analysis for Computer-Aided Diagnosis. Chicago, IL: Univ. Chicago, 2004.
13. B. K. Scurfield, "Multiple-event forced-choice tasks in the theory of signal detectability," J. Math. Psychol., vol. 40, pp. 253-269, 1996.
14. H.-P. Chan, B. Sahiner, L. M. Hadjiiski, N. Petrick, and C. Zhou, "Design of three-class classifiers in computer-aided diagnosis: Monte Carlo simulation study," Proc. SPIE, vol. 5032, pp. 567-578, 2003.