What is it about?
Autoimmune diseases (AID) are a collection of many complex disorders of unknown aetiology. The diagnosis of autoimmune diseases is very complex and is based on the research and identification of antinuclear antibodies (ANA) by indirect immunofluorescence (IIF). The developed method identifies and classifies automatically the Centromere pattern; the method is based on the grouping of centromers present on the cells through a clustering K-means algorithm
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Why is it important?
IIF is a test having high sensitivity, but only analytical and not diagnostic specificity. The quality of the response is strongly influenced by reader's experience, by the quality of reagents used for testing and by other local factors laboratory’s techniques. Identification of antinuclear antibodies through IIF method is an important part of clinical medicine and clinical immunology.
Perspectives
The developed method, using information on the number of discrete fluorescent speckles expected (23-46) has allowed a good identification and classification of the centromere pattern. So, as a future perspective of this work, the method will be adapted for the search of other autoantibody patterns, for which there is knowledge on the distribution of white / dark regions.
Donato Cascio
Universita degli Studi di Palermo
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This page is a summary of: An automated approach for indirect immunofluorescence images classification based on unsupervised clustering method , IET Computer Vision, August 2018, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-cvi.2018.5271.
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