基于NL-Means的双水平集脑部MR图像分割算法

Double Level Set Algorithm Based on NL-Means Denosing Method for Brain MR Images Segmentation

针对脑部MR图像中通常伴有灰度不均、高噪声的缺点,且传统水平集无法有效分割的问题,提出了一种基于NL-Means的双水平集算法。首先,利用改进型NL-Means算法对带有噪声的医学图像进行去噪处理,再通过双水平集算法对图像进行分割,提取多目标区域,为了去除医学图像中灰度不均对分割效果的影响,所提算法引入了偏移场拟合项,进一步改进了双水平集模型,进而对去噪图像分割效果进行了优化处理。实验结果表明,所提算法能有效地解决灰度不均与高噪声的问题,能够将伴有灰度不均的高噪声脑部MR图像完全分割出来,从而获得预期的分割效果。

This paper proposed a novel double level set algorithm based on NL-Means denosing method for brain MR image segmentation,which has a large amount of noise and complicated background,and cannot be separated completely by traditional level set.First of all,this algorithm gets the denoised image by analyzing the image with NL-Means denosing method.Then,the algorithm identifies denoised image by segmenting the analyzed results in terms of improved double level set model.In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the denosing method result.The experimental result shows that the algorithm can reduce the problems of intensity inhomogeneities and noise,can separate brain MR image including intensity inhomogeneities and noise completely,and can obtain the expected effect of segmentation.