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HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information.

Xianbiao Qi, Guoying Zhao, Chun-Guang Li

    IEEE Journal of Biomedical and Health Informatics
    |December 20, 2015
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    Summary

    This study introduces a novel computer-aided diagnosis method for autoimmune diseases using human epithelial type 2 (HEp-2) cell classification. Combining texture and shape features significantly improves classification accuracy, outperforming previous methods.

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    Area of Science:

    • Medical image analysis
    • Computer-aided diagnosis
    • Autoimmune disease diagnostics

    Background:

    • Indirect immunofluorescence imaging of HEp-2 cells is crucial for diagnosing autoimmune diseases.
    • HEp-2 cell classification is challenging due to high intra-class and low inter-class variations.
    • Automated HEp-2 cell classification is an active area of research.

    Purpose of the Study:

    • To develop an effective approach for automatic HEp-2 cell classification.
    • To combine multiresolution co-occurrence texture and large regional shape information for improved classification.
    • To enhance the accuracy of computer-aided diagnosis for autoimmune diseases.

    Main Methods:

    • Proposed a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor for multiresolution texture analysis.
    • Utilized an improved Fisher vector model with RootSIFT features from multi-scale image patches for large regional shape depiction.
    • Combined both texture and shape features for a comprehensive classification approach.

    Main Results:

    • The proposed method achieved superior performance compared to the winners of ICPR 2012 and ICIP 2013 contests.
    • Demonstrated comparable performance to the ICPR 2014 contest winner using a leave-one-specimen-out strategy.
    • Systematically evaluated on ICPR 2012, ICIP 2013, and ICPR 2014 datasets.

    Conclusions:

    • The combination of multiresolution co-occurrence texture and large regional shape information is highly effective for HEp-2 cell classification.
    • The proposed method offers a significant advancement in computer-aided diagnosis of autoimmune diseases.
    • This approach provides a robust and accurate tool for analyzing HEp-2 cell images.