The hypervolume under the ROC hypersurface of "near-guessing" and "near-perfect" observers in N-class classification tasks

IEEE Transactions on Medical Imaging

Volumn 24, Issue 2, 2005, Pages 293-299

  Edwards, Darrin C ^a   Metz, Charles E ^a   Nishikawa, Robert M ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

N dass classification; ROC analysis; ROC performance metrics

Indexed keywords

COMPUTER AIDED DESIGN; DEGREES OF FREEDOM (MECHANICS); MAMMOGRAPHY; NEURAL NETWORKS; OBSERVABILITY; PROBABILITY DENSITY FUNCTION; VECTORS; COMPUTER AIDED DIAGNOSIS; DATA REDUCTION; FUNCTIONS;

N-CLASS CLASSIFICATION; RECEIVER OPERATING CHARACTERISTICS (ROC) ANALYSIS; RECEIVER OPERATING CHARACTERISTICS (ROC) PERFORMANCE METRICS; MAMMOGRAMS; RECEIVER OPERATING CHARACTERISTIC (ROC) ANALYSIS; ROC PERFORMANCE METRICS;

CLASSIFICATION (OF INFORMATION); MAMMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; OBSERVER VARIATION; REPRODUCIBILITY; ROC CURVE; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ROC CURVE; SUBTRACTION TECHNIQUE;

EID: 14744290093     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2004.841227     Document Type: Article

References (22)

1. U. Bick, M. L. Giger, R. A. Schmidt, R. M. Nishikawa, D. E. Wolverton, and K. Doi, "Automated segmentation of digitized mammograms," Acad. Radiol., vol. 2, pp. 1-9, 1995.
2. F.-F. Yin, M. L. Giger, K. Doi, C. E. Metz, C. J. Vyborny, and R. A. Schmidt, "Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images," Med. Phys., vol. 18, pp. 955-963, 1991.
3. F.-F. Yin, M. L. Giger, C. J. Vyborny, K. Doi, and R. A. Schmidt, "Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses," Invest. Radiol., vol. 28, pp. 473-481, 1993.
4. F.-F. Yin, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, "Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral-subtraction technique," Med. Phys., vol. 21, pp. 445-452, 1994.
5. M. A. Kupinski, "Computerized Pattern Classification in Medical Imaging," Ph.D. Thesis, The University of Chicago, Chicago, IL, 2000.
6. Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, R. A. Schmidt, and K. Doi, "Automated computerized classification of malignant and benign masses on digitized mammograms," Acad. Radiol., vol. 5, pp. 155-168, 1998.
7. Z. Huo, M. L. Giger, and C. E. Metz, "Effect of dominant features on neural network performance in the classification of mammographic lesions," Phys. Med. Biol., vol. 44, pp. 2579-2595, 1999.
8. Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, and C. E. Metz, "Computerized classification of benign and malignant masses on digitized mammograms: A study of robustness," Acad. Radiol., vol. 7, pp. 1077-1084, 2000.
9. Z. Huo, M. L. Giger, and C. J. Vyborny, "Computerized analysis of multiple-mammographic views: Potential usefulness of special view mammograms in computer-aided diagnosis," IEEE Trans. Med. Imag., vol. 20, pp. 1285-1292, 2001.
10. Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz, "Breast cancer: Effectiveness of computer-aided diagnosis - Observer study with independent database of mammograms," Radiology, vol. 224, pp. 560-568, 2002.
11. D. J. S. MacKay, "Bayesian Methods for Adaptive Models," Ph.D. Thesis, California Institute of Technology, Pasadena, CA, 1992.
12. M. A. Kupinski, D. C. Edwards, M. L. Giger, and C. E. Metz, "Ideal observer approximation using Bayesian classification neural networks," IEEE Trans. Med. Imag., vol. 20, pp. 886-899, 2001.
13. D. C. Edwards, C. E. Metz, and R. M. Nishikawa, "Estimation of three-class ideal observer decision functions with a Bayesian artificial neural network," in Proc. SPIE, Vol. 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, D. P. Chakraborty and E. A. Krupinski, Eds. Bellingham, WA, 2002, pp. 1-12.
14. D. C. Edwards, L. Lan, C. E. Metz, M. L. Giger, and R. M. Nishikawa, "Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions," Med. Phys., vol 31, pp. 81-90, 2004.
15. 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, pp. 891-895, Jul. 2004.
16. H. L. Van Trees, Detection, Estimation, and Modulation Theory: Part I. New York: Wiley, 1968.
17. D. Mossman, "Three-way ROCs," Med. Decis. Making, vol. 19, pp. 78-89, 1999.
18. S. Dreiseitl, L. Ohno-Machado, and M. Binder, "Comparing three-class diagnostic tests by three-way ROC analysis," Med. Decis. Making, vol. 20, pp. 323-331, 2000.
19. B. K. Scurfield, "Multiple-event forced-choice tasks in the theory of signal detectability," J. Math Psychol., vol. 40, pp. 253-269, 1996.
20. _, "Generalization of the theory of signal detectability to n-event m-dimensional forced-choice tasks," J. Math Psychol., vol. 42, pp. 5-31, 1998.
21. 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," in Proc. SPIE, Vol. 5032, Medical Imaging 2003: Image Processing, vol. 5032, M. Sonka and J. M. Fitzpatrick, Eds. Bellingham, WA, 2003, pp. 567-578.
22. S. I. Grossman, Multivariable Calculus, Linear Algebra, and Differential Equations, 2nd ed. San Diego, CA: Harcourt Brace Jovanovich, 1986.