An Evaluation of LBP Texture Descriptors for the Classification of HEp-2 Cells

Abstract

Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells and consequently important for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. In the literature, various algorithms have been proposed for automatic classification of HEp-2 cells based typically on shape features, texture features and classification algorithms. Local binary pattern (LBP) features are simple yet powerful texture descriptors, which encode the neighbours of a pixels into a binary pattern. While over the years a variety of LBP algorithms have been introduced, only a few descriptors are utilised in the context of HEp-2 cell classification. In this paper, we benchmarked eight rotation invariant LBP variants and a total of 16 descriptors on the ICPR 2012 HEp-2 contest benchmark dataset. We found rotation invariant multi-dimensional LBP features to lead to the best classification performance.