Human Epithelial Type 2 (HEp-2) Cell Classification by Using a Multiresolution Texture Descriptor

Indirect Immunofluorescence Images (IIF) are generated based on a biological response to a specific light spectrum. The analysis of this type of image is helpful for the diagnosis of systemic autoimmune diseases. The Antinuclear Antibodies (ANAs) are detected, in the patient serum, by a specific fluorescence pattern on Human Epithelial type 2 (HEp-2) cells, due to its large range of antigens. In this paper, the two rotation invariant texture descriptors, Sampled Local Mapped Pattern Magnitude (SLMP_M) and Completed Local Binary Pattern Sign and Magnitude (CLBP_S/M) with a multiresolution configuration are investigated to classify 1455 Human Epithelial type 2 cells, extracted from 28 specimen images divided into six pattern categories (centromere, coarse speckled, cytoplasmatic, fine speckled, homogeneous and nucleolar). Results have shown that the multiresolution configuration improves the classification performance and that the SLMP_M descriptor is more suitable and faster to describe this type of images.