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2008
IEEE

Unsupervised segmentation of cell nuclei using geometric models

10 years 4 months ago
Unsupervised segmentation of cell nuclei using geometric models
Fluorescent microscopy of biological samples allows noninvasive screening of specific molecular events in-situ. This approach is useful for investigating intricate signalling pathways and in the drug discovery process. The large volumes of data involved in image analysis are a limiting factor. As manual image interpretation relies on expensive manpower automated analysis is a far more appropriate solution. In this paper we discuss our approach to achieve reliable automated segmentation of individual cell nuclei from wide field images taken of prostate cancer cells. We present a novel analysis routine to accurately identify cell nuclei based upon intensity clustering and morphological validation using a data derived geometric model. This approach is shown to consistently outperform the standard analysis technique using real data.
Shaun Fitch, Trevor Jackson, Peter Andras, Craig R
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where ISBI
Authors Shaun Fitch, Trevor Jackson, Peter Andras, Craig Robson
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