A modified rough-fuzzy clustering algorithm with spatial information for HEp-2 cell image segmentation

Abstract

Indirect immunofluorescence (IIF) analysis is the most effective test for antinuclear autoantibodies (ANAs) analysis, in order to reveal the occurrence of some autoimmune diseases, such as connective tissue disorders. In the tests of antinuclear antibodies, the human epithelial type 2 (HEp-2) cells is mostly used as substrate. However, the recognition of the staining pattern of ANAs in the IIF image requires proper detection of the region of interest. In this regard, automatic segmentation of IIF images is an essential prerequisite as manual segmentation is labor intensive, time consuming, and subjective. Recently, rough-fuzzy clustering has been shown to provide significant results for image segmentation by handling different uncertainties present in the images. But, the existing robust rough-fuzzy clustering algorithm does not consider spatial distribution of the image. This is useful when the image is distorted by noise and other artifacts. In this regard, the paper proposes a segmentation algorithm by incorporating the spatial constraint with the advantages of robust rough-fuzzy clustering. In the current study, class label of a pixel is influenced by its neighboring pixels depending on their spatial distance. In this way, more number of neighboring pixels can be incorporated into the calculation of a pixel feature. The performance of the proposed method is evaluated on several HEp-2 cell images and compared with the existing algorithms by presenting both qualitative and quantitative results.