Restrictions on the three-class ideal observer's decision boundary lines

被引:16
作者
Edwards, DC
Metz, CE
机构
[1] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA
[2] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA
关键词
ideal observers; ROC analysis; three-class classification;
D O I
10.1109/TMI.2005.859212
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We are attempting to develop expressions for the coordinates of points on the three-class ideal observer's receiver operating characteristic (ROC) hypersurface as functions of the set of decision criteria used by the ideal observer. This is considerably more difficult than in the two-class classification task, because the conditional probabilities in question are not simply related to the cumulative distribution functions of the decision variables, and because the slopes and intercepts of the decision boundary lines are not independent; given the locations of two of the lines, the location of the third will be constrained depending on the other two. In this paper, we attempt to characterize those constraining relationships among the three-class ideal observer's decision boundary lines. As a result, we show that the relationship between the decision criteria and the misclassification probabilities is not one-to-one, as it is for the two-class ideal observer.
引用
收藏
页码:1566 / 1573
页数:8
相关论文
共 22 条
[1]   AUTOMATED SEGMENTATION OF DIGITIZED MAMMOGRAMS [J].
BICK, U ;
GIGER, ML ;
SCHMIDT, RA ;
NISHIKAWA, RM ;
WOLVERTON, DE ;
DOI, K .
ACADEMIC RADIOLOGY, 1995, 2 (01) :1-9
[2]   Design of three-class classifiers in computer-aided diagnosis: Monte Carlo simulation study [J].
Chan, HP ;
Sahiner, B ;
Hadjiiski, LM ;
Petrick, N ;
Zhou, C .
MEDICAL IMAGING 2003: IMAGE PROCESSING, PTS 1-3, 2003, 5032 :567-578
[3]   The hypervolume under the ROC hypersurface of "near-guessing" and "near-perfect" observers in N-class classification tasks [J].
Edwards, DC ;
Metz, CE ;
Nishikawa, RM .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2005, 24 (03) :293-299
[4]   Ideal observers and optimal ROC hypersurfaces in N-class classification [J].
Edwards, DC ;
Metz, CE ;
Kupinski, MA .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2004, 23 (07) :891-895
[5]  
EGAN JP, 1975, SIGNAL DETECTION THE
[6]  
FERRI C, 2003, VOLUME ROC SURFACE M
[7]   Breast cancer: Effectiveness of computer-aided diagnosis - Observer study with independent database of mammograms [J].
Huo, ZM ;
Giger, ML ;
Vyborny, CJ ;
Metz, CE .
RADIOLOGY, 2002, 224 (02) :560-568
[8]   Automated computerized classification of malignant and benign masses on digitized mammograms [J].
Huo, ZM ;
Giger, ML ;
Vyborny, CJ ;
Wolverton, DE ;
Schmidt, RA ;
Doi, K .
ACADEMIC RADIOLOGY, 1998, 5 (03) :155-168
[9]   Computerized analysis of multiple-mammographic views: Potential usefulness of special view mammograms in computer-aided diagnosis [J].
Huo, ZM ;
Giger, ML ;
Vyborny, CJ .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2001, 20 (12) :1285-1292
[10]   Computerized classification of benign and malignant masses on digitized mammograms: A study of robustness [J].
Huo, ZM ;
Giger, ML ;
Vyborny, CJ ;
Wolverton, DE ;
Metz, CE .
ACADEMIC RADIOLOGY, 2000, 7 (12) :1077-1084