Automatic segmentation of routinely H&E stained microscopic images by combining support vector machine clustering and active contour models

 
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Technological Educational Institute of Athens
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2004 (EN)
Automatic segmentation of routinely H&E stained microscopic images by combining support vector machine clustering and active contour models (EN)

Σπυρίδωνος, Παναγιώτα Π. (EL)
Γκλώτσος, Δημήτριος (EL)
Κάβουρας, Διονύσης Α. (EL)
Ραβαζούλα, Παναγιώτα (EL)
Αραπαντώνη-Δαδιώτη, Πετρούλα (EL)

Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε. (EL)
Νικηφορίδης, Γεώργιος Χ. (EL)

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. 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. 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. The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images. (EN)

journalArticle

Automation (EN)
Biopsy (EN)
Βιοψία (EN)
Αυτοματοποίηση (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

Analytical and Quantitative Cytology and Histology (EN)

English

2004


Journal of Reproductive Medicine (EN)



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