Pathological lung segmentation in chest CT images based on improved random walker. (March 2021) - Chen, Cheng; Xiao, Ruoxiu; Zhang, Tao; Lu, Yuanyuan; Guo, Xiaoyu; Wang, Jiayu; Chen, Hongyu; Wang, Zhiliang
Highlights: A lung segmentation in chest CT images based on the improved random walker is proposed. The clustering probability is obtained using the Gaussian mixture model (GMM), and the initial value of the GMM is calculated from the improved binary K-means. The spatial distance and the cluster distribution are added to construct new edge weights to build a new random walker map. Based on empirical segmentation theory and the newly constructed map, the initial results were obtained and retained only as intermediate results. When new seeds are introduced, the final segmentation result is quickly calculated from the new parameters and intermediate results. Abstract: Background and Objective: Pathological lung segmentation as a pretreatment step in the diagnosis of lung diseases has been widely explored. Because of the complexity of pathological lung structures and gray blur of the border, accurate lung segmentation in clinical 3D computed tomography images is a challenging task. In view of the current situation, the work proposes a fast and accurate pathological lung segmentation method. The following contributions have been made: First, the edge weights introduce spatial information and clustering information, so that walkers can use more image information during walking. Second, a Gaussian Distribution of seed point set is established to further expand the possibility of selection between fake seed points and real seed points. Finally, the pre-parameter is calculated using original seed points, and the final results are fitted with new seed points. Methods: This study proposes a segmentation method based on an improved random walker algorithm. The proposed method consists of the following steps: First, a gray value is used as the sample distribution. Gaussian mixture model is used to obtain the clustering probability of an image. Thus, the spatial distance and clustering result are added as new weights, and the new edge weights are used to construct a random walker map. Second, a large number of marked points are automatically selected, and the intermediate results are obtained from the newly constructed map and retained only as pre-parameters. When new seed points are introduced, the probability value of the walker is quickly calculated from the new parameters and pre-parameters, and the final segmentation result can be obtained. Results: The proposed method was tested on 65 sets of CT cases. Quantitative evaluation with different methods confirms the high accuracy on our dataset (98.55%) and LOLA11 dataset (97.41%). Similarly, the average segmentation time (10.5s) is faster than random walker (1, 332.5s). Conclusions: The comparison of the experimental results show that the proposed method can accurately and quickly obtain pathological lung processing results. Therefore, it has potential clinical applications.