The color feature is the crucial basis for segmenting a stratum image, but traditional segmentation algorithms based on color feature clustering cannot get desirable results because of colors mixture near the layers' boundaries and heavy noisy. A new image segmentation method is proposed, it adopts an interactive image segmentation algorithm based on Random Walks to improve the computing method of the membership functions of fuzzy C-mean(FCM) incorporating spatial information. It inserts labeled cluster-nodes into the graph formed with pixels and their 4-connectedness, and takes the probabilities that a random walker starting its walk at a pixel first reaches a labeled cluster-node as the membership it belongs to this cluster. The experimental results show that pixels in the area of mixed colors near the layers' boundary are classified more accurately; the method decreases the noise sensitivity of FCM, and can effectively segment the structure physical modeling images.%颜色特征是地层图像分割的重要依据,但地层图像的高噪声以及地层边界颜色混合使得颜色特征空间聚类分割方法无法获得很好的结果.本文提出了一种融合模糊C均值聚类与随机游走算法的图像分割算法,该算法在聚类过程中结合像素的空间信息计算像素的隶属度,在基于随机游走的半监督图像分割算法中像素结点构成的四连通图上插入类属结点作为已标记结点,将随机游走者第一次游走到某个类属结点的概率作为该像素隶属于该类的隶属度.实验结果表明,本算法可以对地层边界颜色混合区域的像素更准确地进行分类,噪声敏感性降低,有效解决构造模拟地层图像的分割问题.
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