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Proceedings Paper

Automatic cell segmentation and classification using morphological features and Bayesian networks
Author(s): Mi-Ra Jung; Jeong-Hee Shim; ByoungChul Ko; Jae-Yeal Nam
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Paper Abstract

This 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.

Paper Details

Date Published: 26 February 2008
PDF: 10 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130G (26 February 2008); doi: 10.1117/12.766202
Show Author Affiliations
Mi-Ra Jung, Keimyung Univ. (South Korea)
Jeong-Hee Shim, Keimyung Univ. (South Korea)
ByoungChul Ko, Keimyung Univ. (South Korea)
Jae-Yeal Nam, Keimyung Univ. (South Korea)


Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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