CAJ | 학술논문

针对图像分割中模糊C均值算法（FCM）无法自动确定聚类中心，不考虑像素邻域信息的问题，提出一种结合k-means的自动FCM图像分割方法。该方法先由图像的灰度直方图确定聚类数目，使用一种改进的快速FCM方法产生初始聚类中心。即通过一步k-means算法对大隶属度灰度更新模糊聚类中心，同时仅对小隶属度灰度使用快速FCM 方法进行隶属度更新，迭代后得到初始聚类中心。利用改进隶属度的FCM算法进行最终聚类。实验表明，该方法获取初始聚类中心接近最终值，加速图像分割，并对噪声具有一定的鲁棒性。
침대도상분할중모호C균치산법（FCM）무법자동학정취류중심，불고필상소린역신식적문제，제출일충결합k-means적자동FCM도상분할방법。해방법선유도상적회도직방도학정취류수목，사용일충개진적쾌속FCM방법산생초시취류중심。즉통과일보k-means산법대대대속도회도경신모호취류중심，동시부대소대속도회도사용쾌속FCM 방법진행대속도경신，질대후득도초시취류중심。이용개진대속도적FCM산법진행최종취류。실험표명，해방법획취초시취류중심접근최종치，가속도상분할，병대조성구유일정적로봉성。
Because fuzzy C-means algorithm(FCM)can not automatically determine the cluster centers, and doesn’t con-sider the neighborhood pixels’information, a new automatic image segmentation method is proposed based on FCM and k-means.The cluster number of the image is obtained by image histogram.And the initial cluster centers are obtained by us-ing an improved fast FCM method.That is, they are obtained by using one step k -means algorithm for large membership degree gray values and only updating the small membership values using fast FCM.It iterates to obtain the initial cluster centers.Image segmentation can be done by using improved membership FCM algorithm.Experiments show that this method generates a closer initial cluster center values to the final clustering centers, reduces the computing time, and has stronger anti-noise property.