{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:37:16Z","timestamp":1760661436342,"version":"build-2065373602"},"reference-count":12,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2006,12,19]],"date-time":"2006-12-19T00:00:00Z","timestamp":1166486400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2007,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010pseudo 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 <jats:italic>Listeria monocytogenes<\/jats:italic>, 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 <jats:italic>Listeria<\/jats:italic> 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 <jats:italic>Listeria<\/jats:italic> in another experiment. After <jats:italic>Listeria<\/jats:italic> infection, ERTR9 macrophages and dendritic cells formed cluster with <jats:italic>Listeria<\/jats:italic> 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. Microsc. Res. Tech., 2007. \u00a9 2006 Wiley\u2010Liss, Inc.<\/jats:p>","DOI":"10.1002\/jemt.20401","type":"journal-article","created":{"date-parts":[[2006,12,19]],"date-time":"2006-12-19T12:17:15Z","timestamp":1166530635000},"page":"171-178","source":"Crossref","is-referenced-by-count":5,"title":["Six\u2010color segmentation of multicolor images in the infection studies of <i>Listeria monocytogenes<\/i>"],"prefix":"10.1002","volume":"70","author":[{"given":"Bin","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng Q.","family":"HeFeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jadwiga","family":"Jablonska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Winkelbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Werner","family":"Lindenmaier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An\u2010Ping","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kurt E.J.","family":"Dittmar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,12,19]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1369\/jhc.4A6355.2005"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"CelenkM Uijt de HaagM.1998.Optimal thresholding for colour images In Proceedings of the SPIE\u2014The International Society for Optical Engineering Nonlinear Image Processing IX 26\u201327 Jan. 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