Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging Using Linear and Kernel-Based Classifiers

A. Schwaighofer, V. Tresp, P. Mayer, A. Scheel, M. Reuss-Borst, A. Krause, I. Mesecke-von Rheinbaben, and H. Rost

Abstract:

We describe a novel system for the examination of patients suffering from rheumatoid arthritis. Basis of this system is a laser imaging technique which is sensitive to the optical characteristics of finger joint tissue. From the laser images acquired at baseline and followup, finger joints can automatically be classified according to whether the inflammatory status has improved or worsened. To perform the classification task, various linear and kernel-based systems were implemented and their performances were compared. From the results presented in this paper, we concluded that the laser-based imaging permits a reliable classification of pathological finger joints, making it a sensitive method for detecting arthritic changes.



Reference: A. Schwaighofer, V. Tresp, P. Mayer, A. Scheel, M. Reuss-Borst, A. Krause, I. Mesecke-von Rheinbaben, and H. Rost. Prediction of rheumatoid joint inflammation based on laser imaging using linear and kernel-based classifiers. IEEE Transactions on Biomedical Engineering, 2002. Accepted for publication.