融合结构特征的增强型FCM图像分割算法

为了使基于模糊C均值(FCM)聚类的图像分割算法对复杂图像更具适用性，将图像结构特征融合到增强型FCM算法.首先，对原始图像进行均值滤波，将滤波结果与原始图像进行线性叠加形成新的输入图像.其次，采用二维Gabor滤波函数提取新的输入图像的纹理结构特征，以此代替灰度特征来衡量节点间的相似性.最后，采用一种改进的节点间距离度量公式来计算图像中节点与聚类中心点的差异.仿真结果表明，对结构复杂的图像所提算法获得了更加精确的分割结果.

Enhanced FCM Algorithm Combined with Structure Feature for Image Segmentation

To improve the ability of fuzzy C means clustering algorithm (FCM) for complex texture structure images， a new fuzzy C means clustering algorithm (En FCM) was proposed by combining image structure features. Firstly， the input image was mean filtered， and the filtered image was added to the original image to form the new image for the subsequent operations. Secondly， the 2 D Gabor filtering function was adopted to extract texture structure feature for the new images to replace the gray level similarity measurement in the traditional FCM algorithm. Finally， a new distance measure function was proposed to calculate the distance between the nodes and the clusters. The simulation results showed that more precise segmentation results could be obtained from complicated texture structure images using the presented algorithm.