针对 FCM 进行图像分割时需要人为确定聚类数的问题，提出一种改进的基于FCM 的图像分割算法．该算法先对图像进行 4 叉树结构的子图分解(即原图等分为 2 ×2的 4 幅子图，子图再等分为 2 ×2 的4 幅子图)，待子图满足一定条件时进行聚类数为 2 的FCM 聚类分割;然后将分割好的区域根据其大小及相邻区域直方图的巴氏距离进行合并，得到最终的分割结果，从而避免了聚类数目的直接确定．实验结果表明:该算法能够获得很好的分割效果;对子图进行聚类分割减少了每次参与聚类的对象数，从而在一定程度上降低了算法的计算量．

The clustering number needs to be determined artificially when image segmentation using FCM is per-formed.In order to solve this problem,an improved algorithm based on FCM is proposed.In this algorithm,first,a sub- image decomposition of the ori- ginal image is conducted on the basis of quad- tree structure,by which theoriginal image is divided into 2 ×2 sub- images equally and each sub- image is divided into 2 ×2 sub- images equallyagain until the sub- image satiates certain conditions.Then,the sub- image is segmented by using FCM with theclustering number 2.Moreover,region merging is carried out according to the region area and the Bhattacharyyadistance of two adjacent regions’histogram,with the segmentation results being finally obtained without determi-ning the clustering number directly.Experimental results indicate that the proposed algorithm possesses good seg-mentation effectiveness,and that,to some extent,the computation complexity is reduced,because the segmenta-tion using FCM helps reduce the number of samples for each clustering.