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Image analysis for quantifying microvessel density in renal cell carcinoma
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
2009 (Korean)In: Journal of Korea Society of Medical Informatics, ISSN 1225-8903, Vol. 15, no 2, 217-225 p.Article in journal (Refereed) Published
Abstract [en]

The most widely used method for quantifying new blood vessel growth in tumor angiogenesis is the determination of microvessel density, which is reported to be associated with tumor progression and metastasis, and a prognostic indicator of patient outcome. In this study, we propose a method for the determination of microvessel density by image analysis, to improve the accuracy and the objectivity of determination of the microvessel density. Four-micron-thick tissue sections of renal cell carcinoma samples were stained immunohistochemically for CD34. The regions with a high degree of vascularization were selected by an expert for digitization. Each image was digitized as a 24-bits/pixel image file with a resolution of 640×480 pixels. First, segmentation of the microvessels based on pixel classification using color features in hybrid color space was performed. After use of a correction process for microvessels with discontinuities and separation of touching microvessels, we counted the number of microvessels for the microvessel density measurement. The result was evaluated by comparison with manual quantification of the same images. The comparison revealed that our computerized microvessel quantification was highly correlated with manual counting by a pathologist. The results indicate that our method is better than the conventional computerized image analysis methods.

Place, publisher, year, edition, pages
2009. Vol. 15, no 2, 217-225 p.
Keyword [en]
Microvessel Density, Color Features, Image Analysis, Computer-assisted Diagnosis
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-114079DOI: 10.4258/jksmi.2009.15.2.217OAI: oai:DiVA.org:uu-114079DiVA: diva2:292821
Available from: 2010-02-09 Created: 2010-02-09 Last updated: 2010-02-11Bibliographically approved

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