Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

Christian D. Klose; Alexander D. Klose; Uwe Netz; Jürgen Beuthan; Andreas H. Hielscher

doi:10.1117/12.809145

20 February 2009 Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

Christian D. Klose, Alexander D. Klose, Uwe Netz, Jürgen Beuthan, Andreas H. Hielscher

Proceedings Volume 7171, Multimodal Biomedical Imaging IV; 71710C (2009) https://doi.org/10.1117/12.809145
Event: SPIE BiOS, 2009, San Jose, California, United States

Abstract

A recent research study has shown that combining multiple parameters, drawn from optical tomographic images, leads to better classification results to identifying human finger joints that are affected or not affected by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints. Additional data are used including, for example, maximum and minimum values of the absorption coefficient as well as their ratios and image variances. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies (AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using ultrasound as gold standard.

Citation Download Citation

Christian D. Klose, Alexander D. Klose, Uwe Netz, Jürgen Beuthan, and Andreas H. Hielscher "Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes", Proc. SPIE 7171, Multimodal Biomedical Imaging IV, 71710C (20 February 2009); https://doi.org/10.1117/12.809145

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