Automatic cell segmentation and classification using morphological features and Bayesian networks

Mi-Ra Jung; Jeong-Hee Shim; ByoungChul Ko; Jae-Yeal Nam

doi:10.1117/12.766202

26 February 2008 Automatic cell segmentation and classification using morphological features and Bayesian networks

Mi-Ra Jung, Jeong-Hee Shim, ByoungChul Ko, Jae-Yeal Nam

Proceedings Volume 6813, Image Processing: Machine Vision Applications; 68130G (2008) https://doi.org/10.1117/12.766202
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

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, Image Processing: Machine Vision Applications, 68130G (26 February 2008); https://doi.org/10.1117/12.766202

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