基于分形几何和最小凸包法的肺区域分割算法
A Lung Region Segmentation Method Based on the Fractal Theory and the Minimal Convex Hull Method

在计算机辅助诊断系统中，为了缩小系统的分析范围、提高计算效率，需要将肺区域分割出来. 但是通过 已有方法获得的肺区域边界不准确，为此提出了一种基于分形几何和最小凸包法的肺区域分割算法．首先，根据肋 骨和各组织的位置关系以及CT 图像的上下层相似的性质，实现了对初始肺区域的自动提取. 其次，利用网格线将 肺区域分成小子块，并计算各子区域块的分形维数. 根据肺区域边界的全局性质和局部性质，构造了最优的分形维 数阈值，并根据该阈值识别需要修复的肺边界. 最后，利用Jarvis 步进法对肺边界进行了修复，从而在CT 图像中获 得了最终的肺区域．通过数值实验证明了提出的算法比传统方法更优秀，具有较高的分割准确率和较高的鲁棒性.
In the computer aided diagnosis systems，the lung region is segmented to reduce the analysis region and increase the computational efficiency. However，the boundary of the lung region obtained by the existing methods is not accurate，thus a lung region segmentation method based on the fractal theory and the minimal convex hull method was proposed. First of all，the lung region was extracted automatically according to the spatial context messages and the position relationship between the rib and other organs. Then the lung region was divided into several blocks by grid lines and the fractal dimension of each block was calculated. Besides，a fractal threshold，which was used to select the correction-needed blocks，was constructed by the global information and local information of the boundary. Finally， the lung edge was corrected by using the Jarvis method. After that，the final lung region was obtained in the CT image. The experiments demonstrate that the proposed method outperforms its traditional counterparts and it has higher segmentation accuracy rate and robustness