Classification of optical tomographic images of rheumatoid finger joints with support vector machines

Vivek Balasubramanyam, Andreas H. Hielscher

    Research output: Contribution to journalConference articlepeer-review


    Over the last years we have developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joint of patients with rheumatoid arthritis (RA). While cross sectional images of the distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In first clinical studies involving 78 finger joints, we compared optical tomographs to ultrasound images and clinical analyses. Receiver-operator curves (ROC) were generated using various image parameters, such as minimum and maximum scattering or absorption coefficients. These studies resulted in specificities and sensitivities in the range of 0.7 to 0.76. Recently, we have trained support vector machines (SVMs) to classify images of healthy and diseased joints. By eliminating redundancy using feature selection, we are achieving sensitivities of 0.72 and specificities up to 1.0. Studies with larger patient groups are necessary to validate these findings; but these initial results support the expectation that SVMs and other machine learning techniques can considerably improve image interpretation analysis in optical tomography.

    Original languageEnglish (US)
    Article number06
    Pages (from-to)37-43
    Number of pages7
    JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
    StatePublished - 2005
    EventAdvanced Biomedical and Clinical Diagnostic Systems III - San Jose, CA, United States
    Duration: Jan 23 2005Jan 26 2005


    • Feature selection
    • Machine learning
    • Optical tomography
    • Rheumatoid arthritis
    • Support vector machines

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Atomic and Molecular Physics, and Optics
    • Biomaterials
    • Radiology Nuclear Medicine and imaging


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