基于分水岭和模糊C均值聚类的图像分割方法

Image segmentation based on watershed and fuzzy C-means clustering

针对分水岭算法对微弱边缘和噪声非常敏感、容易导致过分割现象的问题,提出综合运用分水岭算法和基于区域的模糊C均值聚类的图像分割方法.与单独使用分水岭方法相比,该方法不仅利用区域的灰度信息,而且考虑了区域间的空间信息.实验结果表明,本方法能有效地对图像进行分割,克服了分水岭算法的过分割问题.

Watershed algorithm often causes oversegmentation because of its high sensitivity to the weak edge and the noise. To overcome its limitation, watershed algorithm was combined with region-based fuzzy C-means clustering for image segmentation. Compared with watershed algorithm, not only the grayscale information but also each region＇s spatial information is considered in this method. Experimental results show that this method can efficiently overcome the oversegmentation problem.