Comput Methods Programs Biomed - Automated segmentation of tissue images for computerized IHC analysis.

Tópicos

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Resumo

This paper presents two automated methods for the segmentation of immunohistochemical tissue images that overcome the limitations of the manual approach as well as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.

Resumo Limpo

paper present two autom method segment immunohistochem tissu imag overcom limit manual approach well exist computer techniqu first independ method base unsupervis color cluster recogn automat target cancer area specimen disregard stroma second method base color separ morpholog process exploit autom segment nuclear membran cancer cell extens experiment result real tissu imag demonstr accuraci techniqu compar manual segment addit experi show techniqu effect immunohistochem imag popular approach base supervis learn activ contour propos procedur can exploit applic requir tissu cell explor perform reliabl standard measur activ specif protein involv multifactori genet patholog

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