一种基于改进随机游走的肺结节分割方法

传统的肺结节分割方法无法精确分割出肺结节外部毛刺,并且无法分离出与血管和胸壁相连的肺结节.针对这些问题,提出一种改进的随机游走算法并应用于肺结节的分割中.首先,根据Dirichlet边界条件计算得到的未标记点到标记点的概率值的大小将图像分为目标区域,背景区域和不确定区域,应用欧式距离计算不确定区域中节点与标记点的灰度差异并根据距离的大小将其进行分类.其次,提出了一种两点间抛物线法用于对初始分割结果进行二次分割.实验结果表明,与传统方法相比,所提方法实现了肺结节的精确分割,提高了计算机辅助诊断对肺结节进行分析和鉴别的精度.

Pulmonary Nodules Segmentation Method Based on Improved Random Walker Algorithm

The traditional methods for the segmentation and detection of pulmonary nodules could not segment pulmonary nodules accurately,and also could not separate the pulmonary nodules from blood vessels and chest wall.An improved random walker algorithm was proposed to solve the above problems.Firstly,according to the probability that calculated by Dirichlet boundary condition,the image will be divided into three parts: objective region,background region and uncertain region. Euclidean distance was used to calculate the difference between the nodes in the uncertain region and the seed,which can be used to label nodes in the uncertain region.Secondly,a parabola between two points algorithm(PBTP) for the second segmentation was proposed to achieve the final image segmentation.The experimental results demonstrated that the proposed algorithm segmented the pulmonary nodules more accurately than that of using the traditional methods,which greatly improved the accuracy of analysis and identification for computer-aided diagnosis(CAD) of pulmonary nodules.