基于Level Set方法的医学图像分割

Medical Image Segmentation Based on Level Set Method

对图像分割进行了研究,这是医学图像处理中的关键问题之一.提出了一种结合Fast Marching算法和Watershed变换的医学图像分割方法.首先用非线性扩散滤波对原始图像进行平滑,然后利用Watershed算法对图像进行过度分割,最后用改进的Fast Marching方法对图像进行分割.除此之外,根据区域之间的统计特性的相似度重新定义了Fast Marching方法的速度函数.实验结果表明,该方法能够快速、准确地得到医学图像的分割结果.

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A medical image segmentation method is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed by using nonlinear diffusion filter. Then the smoothed image is over-segmented by the watershed algorithm. Finally, the image is segmented automatically by using the modified fast marching method. Moreover, the speed function is defined based on the statistical similarity degree of the regions. Experimental results show that the algorithm can obtain segmentation result of medical image fast and accurately.