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Unsupervised segmentation of cell nuclei using geometric models

Lookup NU author(s): Simon Fitch, Dr Trevor Jackson, Dr Peter Andras, Professor Craig Robson

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Abstract

Fluorescent microscopy of biological samples allows non-invasive 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. ©2008 IEEE.


Publication metadata

Author(s): Fitch S, Jackson TR, Andras P, Robson CN

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year of Conference: 2008

Pages: 728-731

Date deposited: 28/05/2010

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ISBI.2008.4541099

DOI: 10.1109/ISBI.2008.4541099

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424420032


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