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Automatic cell segmentation and classification using morphological features and Bayesian networks
Proc. SPIE 6813, 68130G (2008); http://dx.doi.org/10.1117/12.766202
Tuesday 29 January 2008
San Jose, CA, USA
Image Processing: Machine Vision Applications
Kurt S. Niel, David Fofi
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Add to FacebookThis paper presents a new approach to the segmentation of the microscopic nuclei images. First, for segmentation of the cell nuclei from background, the adaptive local thresholding is used. A threshold for adaptive local thresholding is estimated by using the gaussian mixture model and maximizing the likelihood function of gray value of cell images. After nuclei segmentation, overlapped nuclei and isolated nuclei need to be classified for exact nuclei separation. For nuclei classification, this paper extracted the morphological features of the nuclei such as compactness, smoothness and moments from training data. For overlapped nuclei classification, this paper uses a Bayesian network with three probability density functions for evidence at each node. The probability density functions for each node are modeled using the three morphological features. After nuclei classification, segmenting of overlapped nuclei into isolated nuclei is necessary. Since watershed algorithm has the problem of over-segmentation, we find makers from each overlapped nuclei and apply watershed algorithm with the proposed merging algorithm. The experimental results using microscopic nuclei images show that our system can indeed improve segmentation performance compared to previous researches, because we performed nuclei classification before separating overlapped nuclei.
© 2008 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Online Feb 26, 2008
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Mi-Ra Jung, Jeong-Hee Shim, ByoungChul Ko and Jae-Yeal Nam, "Automatic cell segmentation and classification using morphological features and Bayesian networks",
Proc. SPIE 6813, 68130G (2008); http://dx.doi.org/10.1117/12.766202
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