We present an approach for the quantification of fluorescent spots in time series of 3-D confocal microscopy
images of endoplasmic reticulum exit sites of dividing cells. Fluorescent spots are detected based on extracted
image regions of highest response using the HMAX transform and prior convolution of the 3-D images with a
Gaussian kernel. The sensitivity of the involved parameters was studied and a quantitative evaluation using
both 3-D synthetic and 3-D real data was performed. The approach was successfully applied to more than one
thousand 3-D confocal microscopy images.
We present an image analysis approach as part of a high-throughput microscopy siRNA-based screening system using cell arrays for the identification of cellular genes involved in hepatitis C and dengue virus replication. Our approach comprises: cell nucleus segmentation, quantification of virus replication level in the neighborhood of segmented cell nuclei, localization of regions with transfected cells, cell classification by infection status, and quality assessment of an experiment and single images. In particular, we propose a novel approach for the localization of regions of transfected cells within cell array images, which combines model-based circle fitting and grid fitting. By this scheme we integrate information from single cell array images and knowledge from the complete cell arrays. The approach is fully automatic and has been successfully applied to a large number of cell array images from screening experiments. The experimental results show a good agreement with the expected behaviour of positive as well as negative controls and encourage the application to screens from further high-throughput experiments.
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