模式识别与人工智能
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  2013, Vol. 26 Issue (5): 498-503    DOI:
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Cell Segmentation in Microscopic Images of Mice BrainBased on Markov Random Field Theory
SUN Li-Ye,HAN Jun-Wei,HU Xin-Tao,GUO Lei
School of Automation,Northwestern Polytechnical University,Xi′an 710072

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Abstract  The neurons in sectioning microscope images of mice brain are important to biologists. Image segmentation algorithms are widely applied to automatically extract the neurons to facilitate further analysis. A method for cell segmentation in microscopic image of mice brain based on Markov Random Field (MRF) theory is proposed. Firstly,manually labeled images and original images are jointly analyzed to estimate the initial parameters in Gaussian Mixture Model,which significantly reduces the number of iterations and increases the precision of segmentation. Secondly,pixel intensity and distance between pixels are integrated into the conventional Potts model to improve the description of the quantitative relationship between pixels. The experimental results demonstrate that the proposed method improves the accuracy and the efficiency of cell segmentation compared to traditional methods.
Key wordsCell Segmentation      Image Segmentation      Markov Random Field      Gaussian Mixture Model      Iterated Conditional Mode     
Received: 31 August 2012     
ZTFLH: TP391  
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SUN Li-Ye
HAN Jun-Wei
HU Xin-Tao
GUO Lei
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SUN Li-Ye,HAN Jun-Wei,HU Xin-Tao等. Cell Segmentation in Microscopic Images of Mice BrainBased on Markov Random Field Theory[J]. , 2013, 26(5): 498-503.
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