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.

This paper proposes an accurate real-time hand tracking and segmentation algorithm. A particle filter tracks the hands in time, based on colour and motion cues. This filter is able to automatically recover from failures and does not need an initialization phase. The algorithm is proven to be robust against lighting changes, and can be used in unconstrained environments. Hand segmentation is based on a Gaussian Mixture Model and refined using a combination of spatial information. Cues from both HSV and RGB colour space are used to increase robustness

The Indirect Immune Fluorescence Test (iIFT) is the most commonly used screening method for the diagnosis of autoimmune diseases. The presence of certain autoimmune diseases is proven by immunologically detecting their corresponding auto-antibodies using the HEp-2 cancer cell line. For this purpose HEp-2 cells are added to the patients' blood serum containing certain auto-antibodies which will bond with the HEp-2 cells leading to a wide variety of patterns that can be observed under a fluorescence microscope. Due to the disadvantages of manual testing, automation and standardization are necessary. This paper proposes an unsupervised segmentation algorithm as part of an ongoing research to develop a CAD system to digitally support iIFT testing.