Effective classification of HEp-2 cells using fuzzy texture descriptors
Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are often categorised into six groups (homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells), which in turn give indications on different autoimmune diseases. While traditionally this classification is performed manually thus representing a subjective and laborous task, recently there is significant interest in computer vision based approaches to automatically categorise HEp-2 cell images and thus provide an objective and fast alternative. Various algorithms have been proposed for this purpose in which texture information often plays a dominant role. In this paper, we also employ texture descriptors for automated classification of HEp-2 cells but choose descriptors that allow to take into account the fuzzy nature of the images and the noise present in them. In particular, we employ a fuzzy version of the well known local binary pattern (LBP) paradigm, coupled with support vector machine (SVM) based classification. We benchmark our approach on the ICPR 2012 HEp-2 contest benchmark dataset and show it to provide excellent classification performance, outperforming not only conventional LBP features but also all algorithms that were entered in the competition as well as the performance of a human expert.