3D ROC Analysis for Medical Imaging Diagnosis

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

doi:10.1109/IEMBS.2005.1616258

3D ROC Analysis for Medical Imaging Diagnosis

Conf Proc IEEE Eng Med Biol Soc. 2005:2005:7545-8. doi: 10.1109/IEMBS.2005.1616258.

Authors

Su Wang¹, C-I Chang, Sheng-Chih Yang, Giu-Cheng Hsu, Hsian-He Hsu, Pau-Choo Chung, Shu-Mei Guo, San-Kan Lee

Affiliation

¹ Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250.

PMID: 17282027
DOI: 10.1109/IEMBS.2005.1616258

Abstract

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.