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Proceedings Paper

A novel partial area index of receiver operating characteristic (ROC) curve
Author(s): Tao Wu; Haibin Huang; Guangwei Du; Yiyong Sun
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Paper Abstract

Appropriate validation of the segmentation algorithms is important for clinical acceptance of those methods. Receiver operating characteristic (ROC) analysis provides the most comprehensive description of the accuracy performance of image segmentation. Total area under an ROC curve (AUC) is widely used as an index of ROC analysis of performance test. However, a large part of the ROC curve is in the clinically irrelevant range. The total area can be misleading in some clinical situation. In this paper, we proposed a partial area index of ROC curves, which measures the segmentation performance in a clinically relevant range decided by learning from subjective ratings. The boundary of the range is defined by a linear cost function of false positive fraction (FPF) and true positive fraction (TPF). The cost factors of FPF and TPF are learned by maximizing the Kendall's coefficient of concordance (KCC) between the partial areas and the subjective ratings. Experiment results show that our method gives a large cost factor on FPF and a small cost factor on TPF on a tumor data set. This is consistent with the fact that a large FPF is generally more difficult to be accepted in tumor segmentation. Our method is able to determine the optimal range for partial area index of ROC analysis, and this partial area index is more appropriate than AUC for evaluating segmentation performance.

Paper Details

Date Published: 6 March 2008
PDF: 8 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 69170B (6 March 2008); doi: 10.1117/12.769888
Show Author Affiliations
Tao Wu, Siemens Ltd. China (China)
Haibin Huang, Siemens Ltd. China (China)
Guangwei Du, Siemens Ltd. China (China)
Yiyong Sun, Siemens Corporate Research (United States)

Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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