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UNSUPERVISED SEGMENTATION OF CELL NUCLEI USING GEOMETRIC MODELS Shaun Fitch, Trevor Jackson, Peter Andras, Craig Robson
 

Summary: UNSUPERVISED SEGMENTATION OF CELL NUCLEI USING GEOMETRIC MODELS
Shaun Fitch, Trevor Jackson, Peter Andras, Craig Robson
Newcastle University, UK
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.

  

Source: Andras, Peter - School of Computing Science, University of Newcastle upon Tyne

 

Collections: Computer Technologies and Information Sciences