为克服现有方法对数字人切片图像分割中人工参与的依赖，提出了一种基于连通域标记和K-均值聚类的数字人脑彩色切片图像分割方法．该方法首先通过连通域标记分割出脑组织的初始区域，再通过腐蚀操作精确提取脑组织，然后在RGB( 红绿蓝) 空间内借助直方图确定聚类中心，以欧几里得距离为判断标准实现对白质的K-均值聚类分割．采用首例中国女性数字化可视人体数据集的序列脑切片图像进行实验，定性和定量分析结果表明，该方法分割准确度高，连续分割性能稳定，能够较好地实现颅脑分离与脑内深度结构的自动提取．

In order to reduce the manual intervention involved in the existing segmentation methods of digital human slice images,an algorithm based on the connected component labeling and the K-means clustering is proposed. In this algorithm,first,the initial region of brain tissue is segmented via the connected component labeling and is refined via erosion. Then,a K-means clustering is adopted to extract the white matter,in which the color histogram is used to determine the clustering centers and the Euclidian distance is considered as the judging criterion. The proposed algorithm is finally applied to the segmentation of the sequential brain slice images from the first Chinese female visible human dataset. The qualitative and quantitative analyses of experimental results indicate that the proposed algorithm is of high segmentation accuracy and strong stability,and that it can be used to the automatic separation of skull from the brain tissue and to the automatic extraction of structures in deep brain.