Texture and shape in fluorescence pattern identification for auto-immune disease diagnosis

V. Snell, W. Christmas, J. Kittler

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


    Automation of HEp-2 cell pattern classification would drastically improve the accuracy and throughput of diagnostic services for many auto-immune diseases, but it has proven difficult to reach a sufficient level of precision. Correct diagnosis relies on a subtle assessment of texture type in microscopic images of indirect immunofluorescence (IIF), which so far has eluded reliable replication through automated measurements. We introduce a combination of spectral analysis and multi-scale digital filtering to extract the most discriminative variables from the cell images. We also apply multistage classification techniques to make optimal use of the limited labelled data set. Overall error rate of 1.6% is achieved in recognition of 6 different cell patterns, which drops to 0.5% if only positive samples are considered.
    Original languageEnglish
    Title of host publicationPattern Recognition (ICPR), 2012 21st International Conference on
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages4
    Publication statusPublished - 14 Feb 2013
    Event21st International Conference on Pattern Recognition - Tsukuba, Japan
    Duration: 11 Nov 201215 Nov 2012


    Conference21st International Conference on Pattern Recognition


    • vectors
    • error analysis
    • shape
    • diseases
    • noise
    • pattern recognition
    • accuracy


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