Automated classification schemes for optical tomographic arthritis scans

Andreas H. Hielscher, Songnan He

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

    Abstract

    We have recently developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joints of patients with rheumatoid arthritis (RA). While cross sectional images of distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In this paper, we apply and analyse two machine learning methods for optimal identification and severity classification of RA in a data set of 78 joints. The methods surveyed include Fisher Face with Support Vector Machines (SVMs), and Transformed Mixtures of Gausians (TMG). It appears that TMG methods outperform the approach using Fisher Face with SVMs; however, the results need to be further validated in studies involving larger patient populations.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1480-1483
    Number of pages4
    ISBN (Print)0780387406, 9780780387409
    DOIs
    StatePublished - 2005
    Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
    Duration: Sep 1 2005Sep 4 2005

    Publication series

    NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
    Volume7 VOLS
    ISSN (Print)0589-1019

    Conference

    Conference2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
    CountryChina
    CityShanghai
    Period9/1/059/4/05

    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

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