HEp-2 cell classification using binary decision tree approach

Human Epithelial Type-2 (HEp-2) cells have been accepted as substrates for antinuclear antibodies (ANA) detection by Indirect ImmunoFluorescence (IIF) method in the blood serum of a patient. In order to diagnose the autoimmune diseases, specialists observe the fluorescencepattern of HEp-2 cells by examining the IIF slides using the fluorescence microscope and then generate the report. The reliability of the ANA screening can be enhanced by a computer aided analysis (CAD), as it is of intuitive nature. In preprocessing green channel image was extracted as it contains more information, noise was removed using Weiner filter and contrast stretching was performed for better analysis. This paper proposed a binary decision tree approach for the HEp-2 cell classification, which can be embedded into CAD system used for ANA detection. This approach relied on texture descriptors, which were proved to be the effective differentiators for HEp-2 cell patterns. From the experimental result, it was observed that binary decision tree approach yields an accuracy of 97.83% on the available IIF HEp-2 cell dataset.