Rough-fuzzy segmentation of HEp-2 cell indirect immunofluorescence images

Article Type

Research Article

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International Journal of Data Mining and Bioinformatics


Human epithelial type-2 (HEp-2) cell is currently the most recommended substrate in indirect immunofluorescence (IIF) tests to diagnose various connective tissue disorders. The IIF test identifies the presence of antinuclear antibody (ANA) in patient serum. However, the proper detection of HEp-2 cells from the IIF images is an important prerequisite for the recognition of staining patterns of ANAs. The characteristics of HEp-2 cell images, due to fluorescence intensity, make the segmentation process more challenging. Recently, rough-fuzzy clustering algorithms have been shown to provide significant results for image segmentation by handling different uncertainties present in the images. But, the neighbourhood information is completely ignored in these algorithms. However, the spatial information is useful when the image is distorted by different imaging artefacts. In this regard, the paper presents a segmentation algorithm by incorporating the neighbourhood information into rough-fuzzy clustering algorithm. In the current study, the class label of a pixel is influenced by its neighbouring pixels, depending on their local spatial constraint and local grey level constraint. The performance of the proposed method is evaluated on several HEp-2 cell IIF images and compared with that of existing algorithms, both qualitatively and quantitatively.

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