一种偏置场环境下的模糊聚类分割算法

A Modified Fuzzy C-Means Segmentation Algorithm under Bias Field

针对偏置环境下图像分割问题,提出了一种基于偏置场估计的模糊聚类算法。通过建立依赖于偏置场的模糊聚类目标函数,提出了模糊聚类隶属函数和偏置场估计的迭代算法。该方法较好地处理了传统模糊聚类在偏置场存在的情况下图像分割精度下降问题。实验结果表明,该算法能有效分割具有偏置噪声的图像,其分割精度优于传统模糊聚类法。

A novel FCM segmentation algorithm is proposed based on bias field estimation with respect to the segmeritation issue of defocused images with illumination patterns under bias field.Firstly the recursive algorithms are provided for bias field estimation,clustering centers and fuzzy clustering function based on intensity and average intensity of pixel neighborhood based on object function of image model with intensity inhomogeneity,A dilation operator is then used on initial segmented image for further refinement,The problem of reduced segmentation accuracy with traditional FCM is well solved by the proposed approach.Experiment results show that the proposed method is effectiveness for illumination pattern based images under bias field and of higher accuracy rates than traditional FCM.