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Classification of human Epithelial Type-2 cells using hierarchical segregation

Identifying the presence of Anti-Nuclear Antibody (ANA) in Human Epithelial Type-2 (HEp-2) cells via Indirect Immunofluoresence (IIF) images is commonly used to detect various diseases in clinical pathology tests. The main task at hand is the classification of cells into different categories by observing their staining patterns. However, when performed manually, this method is time and labour intensive. Also, as IIF analysis is still subjective, medical doctors have not been able to get satisfactory accuracy rates on the classification task. Pattern recognition techniques have been introduced, but performance of current systems in literature are not satisfactory as they use a common set of descriptors to describe images from all classes and perform classification in one go. Here, we propose a system based on hierarchical classification that takes into account discriminative features for classification purpose. A new texture descriptor based on change of curvature of the parametric from of image intensity is proposed. We have also introduced a model adaptation technique using non-linear transformation functions to make the classifiers more robust.