HEp-2 Cells Classification via Fusion of Morphological and Textural Features

Autoimmune diseases are proven to be connected with the occurrence of autoantibodies in patient serum. Antinuclear autoantibodies (ANAs) identification can be accomplished in a laboratory using indirect immunofluorescence (IIF) imaging. ANAs are characterized by specific "visual" patterns on a humane epithelial cell line (HEp-2). The identification stage is usually done by trained and highly qualified physicians through visual inspection of slides using a fluorescence microscope. The presence of subjectivity in the identification process, the inter-observer variability, the increasing demand of highly specialized personnel, suggest that a realization of an automatic classification system is of great significance for the field of autoimmune diseases diagnosis. Moreover CAD systems can be used in a collaborative scheme in order to augment the physicians' capabilities. In this paper a system for automatic classification of staining patterns on single-cell fluorescence images is proposed. Our method utilizes morphological features extracted from a set of binary images derived via multi-level thresholding of fluorescence images. Furthermore, a modified version of Uniform Local Binary Patterns descriptor is incorporated in order to capture local textural information. The classification is performed using a non-linear SVM Classifier. The proposed method is evaluated using a publicly available dataset, recently released for the purposes of HEP-2 Cells classification competition at ICPR 2012, achieving up to 95.9% overall classification accuracy.