基于各向异性滤波和空间FCM的MRI图像分割方法

MRI image segmentation method based on anisotropic diffusion and spatial FCM

针对具复杂目标和边界模糊的MRI图像中多感兴趣区域的分割中分割MRI图像软组织难的问题,提出了一种基于各向异性滤波和空间模糊C-均值聚类(SFCM)的MRI图像分割方法;用新型各向异性滤波对图像进行预处理,解决去噪平滑的同时弱化图像细节的问题;用邻域空间信息设计空间函数,改进传统FCM的目标函数;用图像的空间信息实现图像各目标准确分类、有效解决孤立区域的正确归类问题,进而使分割区域完整;用直方图拟合曲线初始化分类数和初始聚类中心,加快算法迭代到最优解,进而减少运行时间。通过实验证实了各向异性滤波和空间FCM的MRI图像分割方法的综合应用显著提高了分割灰度重叠、目标不连续和目标边界模糊的MRI图像的分割效果。

To resolve the difficult problem of soft tissue segmentation of MRI images of the segmentation of the multi target region of interest in the MRI images with complex targets and fuzzy boundary, this paper proposed a novel MRI image segmentation method, based on anisotropic diffusion and spatial fuzzy C-means clustering （SFCM）. It preprocessed the images using the nonlinear and anisotropic diffusion, resolving the problem of weakening the image details while removing the noise. It designed space function combining with the neighborhood space, improving traditional FCM objective function. It used the spatial information of the image to achieve the accurate classification of every object in image was an effective solution to the isolated area correctly classified. After that, it obtained complete and continuous segmented regions. Finally, it utilized the fitting curve of histogram to initialize the classification number and the initial cluster centers, accelerating the algorithm iterative to the optimal solution, and also reducing the run time. The experiments show that the possibility of find a best solution is improved by introducing the method of MRI image segmentation method based on anisotropic diffusion and spatial FCM, so as to the processing of MRI images segmentation which has overlapped grayscale, discontinuous objects and fuzzy boundary.