Indirect immunofluorescence (IIF) imaging is the recommended laboratory technique to detect autoantibodies in patient serum, but it suffers from several issues limiting its reliability and reproducibility. IIF slides are observed by specialists at the fluorescence microscope, reporting fluorescence intensity and staining pattern and looking for mi-totic cells. Indeed, the presence of such cells is a key factor to assess the correctness of slide preparation process and the reported staining pattern. Therefore, the ability to detect mitotic cells is needed to develop a complete computer-aided-diagnosis system in IIF, which can support the specialists from image acquisition up to image classification. Although recent research in IIF has been directed to image acquisition, image segmentation, fluorescence intensity classification and staining pattern recognition, no works presented methods suited to classify such cells. Hence, this paper presents an heterogeneous set of features used to describe the peculiarities of mitotic cells and then tests five classifiers, belonging to different classification paradigms. The approach has been evaluated on an annotated dataset of mitotic cells. The measured performances are promising, achieving a classification accuracy of 86.5 %.
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