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CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images

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Abstract

The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.

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Acknowledgements

This work was partially supported by the Department of Science and Technology, Government of India (Grant No. SB/S3/EECE/050/2015). The authors would like to thank Debamita Kumar of Indian Statistical Institute, Kolkata, for her valuable experimental support.

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  1. Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Ankita Mandal & Pradipta Maji

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  1. Ankita Mandal
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  2. Pradipta Maji
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Correspondence to Pradipta Maji.

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Mandal, A., Maji, P. CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images. Neural Comput & Applic 32, 16471–16489 (2020). https://doi.org/10.1007/s00521-019-04108-w

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