HEp-2 fluorescence pattern classification

V. Snell, W. Christmas, J. Kittler

    Research output: Contribution to journalArticlepeer-review


    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 has, so far, eluded reliable replication through automated measurements. Following the recent HEp-2 Cells Classification contest held at ICPR 2012, we extend the scope of research in this field to develop a method of feature comparison that goes beyond the analysis of individual cells and majority-vote decisions to consider the full distribution of cell parameters within a patient sample. We demonstrate that this richer analysis is better able to predict the results of majority vote decisions than the cell-level performance analysed in all previous works. ?? 2014 Elsevier Ltd. All rights reserved.
    Original languageEnglish
    Pages (from-to)2338-2347
    Number of pages10
    JournalPattern Recognition
    Issue number7
    Early online date19 Oct 2013
    Publication statusPublished - 1 Jul 2014


    • IIF image
    • HEp-2 pattern
    • texture


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