References of "Winkelbach, Simon"
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See detailUse of Autostitch for automatic stitching of microscope images.
Ma, Bin; Zimmermann, Timo; Rohde, Manfred et al

in Micron (2007), 38(5), 492-9

Image stitching is the process of combining multiple images to produce a panorama or larger image. In many biomedical studies, including those of cancer and infection, the use of this approach is highly ... [more ▼]

Image stitching is the process of combining multiple images to produce a panorama or larger image. In many biomedical studies, including those of cancer and infection, the use of this approach is highly desirable in order to acquire large areas of certain structures or whole sections, while retaining microscopic resolution. In this study, we describe the application of Autostitch, viz. software that is normally used for the generation of panoramas in photography, in the seamless stitching of microscope images. First, we tested this software on image sets manually acquired by normal light microscopy and compared the performance with a manual stitching approach performed with Paint Shop Pro. Secondly, this software was applied to an image stack acquired by an automatic microscope. The stitching results were then compared with that generated by a self-programmed rectangular tiling macro integrated in Image J. Thirdly, this program was applied in the image stitching of images from electron microscopy. Thus, the automatic stitching program described here may find applications in convenient image stitching and virtual microscopy in the biomedical research. [less ▲]

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See detailSix-color segmentation of multicolor images in the infection studies of Listeria monocytogenes.
Ma, Bin; He, Feng UL; Jablonska, Jadwiga et al

in Microscopy Research and Technique (2007), 70(2), 171-8

Multiple immunofluorescent staining is a powerful strategy for visualizing the spatial and temporal relationship between antigens, cell populations, and tissue components in histological sections. To ... [more ▼]

Multiple immunofluorescent staining is a powerful strategy for visualizing the spatial and temporal relationship between antigens, cell populations, and tissue components in histological sections. To segment different cell populations from the multicolor image generated by immunostaining based on color addition theory, a systems approach is proposed for automatic segmentation of six colors. After image acquisition and processing, images are automatically segmented with the proposed approach and six-pseudo channels for individual or colocalized fluorescent dye are generated to distinguish different cell types. The principle of this approach is the classification of each pixel into one of six colors (red, green, blue, yellow, magenta, and cyan) by choosing the minimal angular deviation between the RGB vector of the given pixel and six classically defined edge vectors. In the present infection studies of Listeria monocytogenes, the new multicolor staining methods based on the color addition were applied and the proposed color segmentation was performed for multicolor analysis. Multicolor analysis was accomplished to study the migration and interaction of Listeria and different cell subpopulations such as CD4CD25 double positive T regulatory cells; we also visualized simultaneously the B cells, T cells, dendritic cells, macrophages, and Listeria in another experiment. After Listeria infection, ERTR9 macrophages and dendritic cells formed cluster with Listeria in the infection loci. The principle of color addition and the systems approach for segmentation may be widely applicable in infection and immunity studies requiring multicolor imaging and analysis. This approach can also be applied for image analysis in the multicolor in vivo imaging, multicolor FISH or karyotyping or other studies requiring multicolor analysis. [less ▲]

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