To overcome the shortcomings of the traditional FCM algorithm

such as the difficulty to determine cluster numbers slow iteration

and the tendency to plunge into local optimization

as well as sensitivity to

the initial values

an improved fuzzy c-means clustering combining with the global best harmony search algorithm(GBHS-FCM) is proposed. First

the initial cluster numbers and cluster centers of the FCM algorithm are obtained by the GBHS algorithm

while taking the advantages of global superiority and robustness of the GBHS algorithm. Then a new fuzzy clustering function is presented by combining the pixel intensity information and the spatial dependence to the neighboring pixels together

which enhances the spatial continuity of the segmentation results. Finally

a new distance formula is proposed to replace the traditional Euclidean distance formula

which enhances the robustness of the new algorithm to noise. The simulation results show that the GBHS-FCM algorithm performs better than FCM algorithm in accuracy

speed and robustness.