基于随机游走算法的CT图像肺肿瘤分割

Large lung tumor segmentation in CT images based on random walk algorithm

针对CT图像肺肿瘤分割中复杂大肿瘤分割的准确性和自适应问题,提出了一种基于随机游走算法的分割方法.首先,根据图像灰度信息提取肺实质;针对大肿瘤与周围肺组织粘连的复杂情况,先提取有凹陷的肺实质,再根据肺实质先验轮廓,用曲线段形变模型修补肺实质的凹陷边界.然后,用区域生长法自动确定目标种子点和背景种子点;对于大血管与肿瘤粘连的情况,需要少量交互修改个别背景种子点.最后,用随机游走算法完成大肿瘤的分割.实验结果表明,该方法的准确性高,分割结果能够满足临床治疗效果分析和病理学研究的要求.

To the segmentation accuracy and adaption of large lung tumors with complex structures in CT images, a segmentation method based on random walk algorithm is proposed. First, the lung parenchyma was extracted according to the gray level information. If the large tumors adhered to normal tissues surrounding lungs, the lung parenchymas with concave edges are first extracted, and then the lung parenchyma with concave edges is refined by the curve section deformation model according to the prior shape of lung. Then a random walk algorithm is implemented, where the object and background seeds are automatically found by region growing method,and interaction are needed to modify a few background seed points if vasa adhere to tumors. The experiment results show that this method is accurate, which can meet the requirements of clinical treatment and pathological analysis.