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  4. Τμήμα Ιατρικής
  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΙΑΤ
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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/23574
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dc.contributor.authorGlotsos, D.en
dc.contributor.authorSpyridonos, P.en
dc.contributor.authorCavouras, D.en
dc.contributor.authorRavazoula, P.en
dc.contributor.authorDadioti, P. A.en
dc.contributor.authorNikiforidis, G.en
dc.date.accessioned2015-11-24T19:33:49Z-
dc.date.available2015-11-24T19:33:49Z-
dc.identifier.issn0884-6812-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/23574-
dc.rightsDefault Licence-
dc.subjectAlgorithmsen
dc.subjectAstrocytoma/*pathologyen
dc.subjectAutomationen
dc.subjectBiopsyen
dc.subjectCell Nucleus/pathologyen
dc.subjectEosine Yellowish-(YS)/*diagnostic useen
dc.subjectHematoxylin/*diagnostic useen
dc.subjectHumansen
dc.subject*Image Interpretation, Computer-Assisteden
dc.subject*Image Processing, Computer-Assisteden
dc.subjectReproducibility of Resultsen
dc.subjectStaining and Labelingen
dc.subjectUrinary Bladder Neoplasms/*pathologyen
dc.titleAutomated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour modelsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/15678615-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.publicationDate2004-
heal.abstractOBJECTIVE: To develop a method for the automated segmentation of images of routinely hematoxylin-eosin (H-E)-stained microscopic sections to guarantee correct results in computer-assisted microscopy. STUDY DESIGN: Clinical material was composed 50 H-E-stained biopsies of astrocytomas and 50 H-E-stained biopsies of urinary bladder cancer. The basic idea was to use a support vector machine clustering (SVMC) algorithm to provide gross segmentation of regions holding nuclei and subsequently to refine nuclear boundary detection with active contours. The initialization coordinates of the active contour model were defined using a SVMC pixel-based classification algorithm that discriminated nuclear regions from the surrounding tissue. Starting from the boundaries of these regions, the snake fired and propagated until converging to nuclear boundaries. RESULTS: The method was validated for 2 different types of H-E-stained images. Results were evaluated by 2 histopathologists. On average, 94% of nuclei were correctly delineated. CONCLUSION: The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images.en
heal.journalNameAnal Quant Cytol Histolen
heal.journalTypepeer-reviewed-
heal.fullTextAvailabilityTRUE-
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