修正与融合的肺部分割算法

为了能在适当的计算复杂度下获得较好的分割效果，在总结现有方法优缺点的基础上提出了将简单方法进行修正与融合的肺部分割方法.首先使用聚类的方法将CT图像中的像素点分为亮与暗两类，获得灰度值不同的小区域，然后区分出初始肺部的大致区域，最后根据CT图像的具体特性，使用识别出的初始肺部区域边界附近像素点的梯度以及灰度信息对边界进行修正.使用上述综合方法对CT图像的处理结果表明，在计算复杂度不是太大的情况下能获得较好的分割效果.

Modify and Integrate Lung Segmentation Algorithm

In order to obtain better segmentation results under acceptable computational complexity， the advantages and disadvantages of the existing methods are summarized， and then the lung segmentation method which modifies and integrates the simple methods is proposed. Firstly， the clustering method is used to classify the pixels in CT images into two categories， and the small regions with different gray values are obtained. And then the initial region of the lung is identified. Finally， according to the specific characteristics of the CT image， the boundary is corrected by using the gradient and the gray information of the pixels near the initial lung boundary. The processing results of CT images using the above synthesis method show that the segmentation results can be better when the computational complexity is not too large.