基于FCM和LBF模型的医学图像自动分割

基于局部区域二相拟合(LBF)模型的医学图像分割方法，对初始轮廓敏感并仅能分割单类目标，若手动选取的初始轮廓不合适，将导致算法耗时过大甚至分割失败。针对上述不足，提出联合模糊C均值(FCM)聚类的LBF模型自动分割算法。对待分割图像进行FCM聚类，将得到的目标类隶属度值变换为适用于LBF模型的水平集函数初始值，利用LBF模型从该初始值开始演化直至收敛，从而完成分割。合成图像及血管和脑部图像的分割实验结果表明，该算法能够自动获取合适的初始值，有效解决LBF模型对初始轮廓敏感的问题，减少迭代次数，而且通过选择不同的FCM聚类结果，可以实现对多类目标的分割。

Automated Medical Image Segmentation Based on FCM and LBF Model

The medical image segmentation based on Local Binary Fitting(LBF) model is sensitive to initial contour and merely available to single object. If its initial contour chosen manually is not suitable, the segmentation needs too much CPU time and sometimes is even unsuccessful. To overcome these disadvantages, an integrate Fuzzy C-means(FCM) clustering into LBF model is proposed for automated image segmentation. The image to be segmented is clustered into objects and background using FCM algorithm, from which the resulted fuzzy membership of each object is transformed into the initial value of level set function with respect to the LBF model. Starting from the initial value, the evolution of LBF model is continued until convergence. Thus, the segmentation is accomplished. Experimental results on the synthetic and real images(blood vessel images and the brain image) show that the proposed algorithm can get the suitable initial value automatically. As a result, the sensitivity to the initial contour is solved effectively and the iteration number is also decreased considerably. Moreover, the multiple objects segmentation can be implemented by choosing the different cluster generated previously from FCM algorithm.