在人类视觉阈值选择模型基础上，结合C均值聚类思想，提出一种基于人类视觉模型的区域生长图像分割算法。根据人类视觉模型选取初始种子，并自适应调整区域生长的相似性准则，既从全局考虑了种子的生长对误差平方和的影响，又从局部考虑了像素的邻域相似度信息，实现了类似于边缘的限制效果。实验表明，即使在复杂背景下，该方法依然能得到接近人眼视觉特性的分割效果，且具有较高的执行效率。

A new image segmentation algorithm combining region growing with C-means Clustering is proposed based on human visual model. Initial seeds are selected automatically and the similarity principle of region growing is adjusted adaptively according to human visual threshold effect. By considering the global influence of region growing to error sum of squares, as well as the local similarity information of seeds’ neighborhood, the proposed algorithm can limit seeds to grow within object boundaries. Experimental results show that the proposed method can produce better segmentation performance with less computational complexity than traditional methods.