Artificial Intelligence in Medicine
Volume 40, Issue 2 , Pages 65-85, June 2007

Automated segmentation and quantification of inflammatory tissue of the hand in rheumatoid arthritis patients using magnetic resonance imaging data

  • Evanthia E. Tripoliti

      Affiliations

    • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina and Biomedical Research Institute - FORTH, GR 451 10 Ioannina, Greece
    • ,
  • Dimitrios I. Fotiadis

      Affiliations

    • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina and Biomedical Research Institute - FORTH, GR 451 10 Ioannina, Greece
    • Corresponding Author InformationCorresponding author at: Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece. Tel.: +30 26510 98803; fax: +30 26510 98890.
    • ,
  • Maria Argyropoulou

      Affiliations

    • Department of Radiology, Medical School, University of Ioannina, GR 451 10 Ioannina, Greece

Received 24 July 2006; received in revised form 19 January 2007; accepted 8 February 2007.

Summary 

Objectives

The aim of this paper is the development of an automated method for the segmentation and quantification of inflammatory tissue of the hand in patients suffering form rheumatoid arthritis using contrast enhanced T1-weighted magnetic resonance images.

Methods and materials

The proposed automatic method consists of four stages: (a) preprocessing of images, (b) identification of the number of clusters, by minimizing the appropriate validity index, (c) segmentation using the fuzzy C-means algorithm employing four features which are related to intensity and the location of pixels and (d) postprocessing, where defuzzification is performed and small objects and vessels are eliminated and quantification takes place.

Results

The proposed method is evaluated using a dataset of image sequences obtained from 25 patients suffering from rheumatoid arthritis. For 17 of them we have obtained follow-up images after 1 year treatment. The obtained sensitivity and positive predictive rate is 97.71% and 83.35%, respectively. In addition, quantification of inflammation before and after treatment, as well as, comparison with manual segmentation is carried out.

Conclusions

The proposed method performs very well and results in high detection and quantification accuracy. However, the reduction of false positives and the identification of old inflammation must be addressed.

Keywords: Rheumatoid arthritis, Fuzzy C-means clustering, Magnetic resonance imaging

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PII: S0933-3657(07)00016-4

doi:10.1016/j.artmed.2007.02.003

Artificial Intelligence in Medicine
Volume 40, Issue 2 , Pages 65-85, June 2007

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