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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMultimodal Biomedical Imaging IV
StatePublished - 2009
EventMultimodal Biomedical Imaging IV - San Jose, CA, United States
Duration: Jan 24 2009Jan 26 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceMultimodal Biomedical Imaging IV
Country/TerritoryUnited States
CitySan Jose, CA


  • Classification
  • Computer-aided
  • Multi-parameter
  • Rheumatoid arthritis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes'. Together they form a unique fingerprint.

Cite this