Automatic Acquisition of Immunofluorescence Images: Algorithms and Evaluation Paolo Soda, Amelia Rigon, Antonella Afeltra, Giulio Iannello Università Campus Bio-Medico di Roma, Via Longoni 83, 00155, Roma, Italy { p.soda, a.rigon, a.afeltra, g.iannello }@unicampus.it Abstract In this paper, we report our experience in the development of a system for automatic acquisition of Immuno-Fluorescence Assay (IFA) images. We focus on two basic issues. Firstly, we determine an autofocus function that can deal with photobleaching, a physical phenomenon affecting automatic acquisition of IFA images, and present a set of experiments on real images that confirm its effectiveness. Secondly, we discuss if the physicians may reliably use digital IFA images in place of direct microscope observations to carry out the diagnosis. In this respect, we present the results of a preliminary experiment where physicians perform the diagnosis on a set of images both by looking directly to them at the fluorescence microscope and by looking at digital images on the screen of a workstation. 1. Introduction Connective tissue diseases (CTD) are autoimmune disorders, commonly marked by serum antinuclear autoantibodies (ANA). The recommended method for ANA testing is ImmunoFluorescence Assay (IFA) [1], [2]. The tests are examined at the fluorescence microscope to reveal the antigen-antibody reaction. In IFA diagnosis, the physician has to report two-pieces of information: fluorescent intensity and pattern description. The readings in IFA are subjected to interobserver variability which limits the reproducibility of the method. To date, the highest level of automation in IFA tests is the preparation of slides with robotic devices performing dilution, dispensation and washing operations [3], [4]. Hence, the development of a system to support physician decision may offer a solution and this is an evident medical demand [2]. In this respect, the ability to automatically and reliably acquire IFA images seems a basic milestone. In this paper, we focus on two issues related with acquisition of IFA images. The first one is the determination and validation of an autofocus procedure to cope with photobleaching, a physical phenomenon that affects automatic acquisition of IFA images and limits the application of existing methods. The second issue is the effectiveness of automatically acquired digital images for diagnostic purposes, i.e. if the physicians may reliably use digital IFA images in place of direct microscope observations to carry out the diagnosis. In this respect we present the encouraging results of a preliminary experiment where physicians perform the diagnosis on a set of images by looking both at the fluorescence microscope and at digital images on the screen of a workstation. 2. Materials For appropriate IFA tests, current guidelines recommend the use of tumour cell line (HEp-2) substrate [1], [2] with the 1:80 titer. The images are taken by an acquisition unit consisting of the fluorescence microscope by Leica, equipped both with a 50 W mercury vapour lamp and with a monochrome CCD camera, which has squared pixels of equal side to 6.45 m. The objective is a 40x and the medium is the air. The exposure time of slides to incident light is 0.4 s. The images have a resolution of 1024x1344 pixels and a colour-depth of 8 bits; they are stored in TIFF format. We look digital images on a 19’’ flat monitor HP L1940. Monitor settings are 1280x1024 pixels and refresh rate of 60 Hz. 3. The autofocus algorithm Focus algorithms proposed in literature [5]-[10] are based on a criterion function applied to images of the same sample, acquired at different z-axis positions. The autofocus functions give a value that indicates the degree of focusing of each image. The function maximum should correspond to the optimum focus position, and it should be sharp enough to make easy its localization. In a typical focus function three different regions can be distinguished: a near-flat region, a sloped region and a quadratic region, which lies just around the function peak [9]. The algorithm is therefore organized according three subsequent phases named coarse, fine and refine, respectively. Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) 0-7695-2517-1/06 $20.00 © 2006 IEEE