3D ROC Analysis for Medical Imaging Diagnosis.

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3D ROC Analysis for Medical Imaging Diagnosis. Abstract Text:

Su Wang,C-I Chang,Sheng-Chih Yang,Giu-Cheng Hsu,Hsian-He Hsu,Pau-Choo Chung,Shu-Mei Guo,San-Kan Lee,

Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detection power interpreted as false positive (FP) and true positive (TP) respectively in terms of medical diagnosis. The ROC curve is plotted based on TP versus FP via hard decisions. This paper presents a three dimensional (3D) ROC analysis which extends the traditional two-dimensional (2D) ROC analysis by including a threshold parameter in a third dimension resulting from soft decisions, (SD). As a result, a 3D ROC curve can be plotted based on three parameters, TP, FP and SD. By virtue of such a 3D ROC curve three two-dimensional (2D) ROC curves can be derived, one of which is the traditional 2D ROC curve of TP versus FP with SD reduced to hard decision. In order to illustrate its utility in medical diagnosis, its application to magnetic resonance (MR) image classification is demonstrated.

3D ROC Analysis for Medical Imaging Diagnosis. Publishing Authors By Initials

S Wang,CI Chang,SC Yang,GC Hsu,HH Hsu,PC Chung,SM Guo,SK Lee,

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3D ROC Analysis for Medical Imaging Diagnosis. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Conference proceedings : ... Annual International

VOLUME: 7

Page Numbers: 7545-8

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ISSN: 1557-170X

DAY: 6

MONTH: 02

YEAR: 2005

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LANGUAGE: eng

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AFFILIATION: Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250.

Country: United States

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MEDLINETA: Conf Proc IEEE Eng Med Biol So

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