Algorithmic framework for HEp-2 fluorescence pattern classification to aid auto-immune diseases diagnosis

Abstract

Fluorescence microscopy allows the acquisition of the spectroscopic properties of fluorescent reporter molecules at levels of resolution too small to be seen with the naked eye. The Indirect Immune Fluorescence Test is the method used to identify antinuclear antibodies. The main principle of this method is to identify the auto-antibodies in a patient's blood serum by staining affected cell structures. The resulting auto-antibody specific fluorescence patterns can be visualized by a fluorescence microscope and examined by a physician to determine a diagnosis. More than 30 different nuclear and cytoplasmic fluorescence patterns are known, which are characterized by a set of a 100 different auto-antibodies. The quality of a suspicion diagnosis strongly depends on the experience of the physicians and, as such, can be very subjective. This paper focuses on the development and evaluation of image processing and classification algorithms for HEp-2 Cell segmentation and cell type classification in order to better detect a suspicion diagnosis for auto-immune diseases.