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中文摘要：
鼠脑中的神经细胞是生物学家的一个重要研究对象．随着计算机视觉技术的飞速进步，研究者们利用图像分割技术从鼠脑切片显微图像自动提取细胞，为进一步分析提供便利．文中提出一种基于马尔可夫随机场理论的鼠脑切片细胞分割算法．相对于传统的算法，文中创新是利用已有的专家标记图和原始图像的灰度特征，结合期望最大化算法，初步估计高斯混合模型的参数，作为条件迭代模式算法的初始值，不仅提高分割精度，且减少迭代次数；并将像素的灰度特征和像素间的距离加入到传统的Potts随机场模型中，更加合理地描述像素间的定量关系．实验结果表明，与传统方法相比，此方法具有较高的计算效率和分割精度．
英文摘要：
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.
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