基于约束随机游走的肿瘤图像分割方法

Tumor Image Segmentation Method Based on Random Walk with Constraint

精确的肺部肿瘤区域分割对于放射治疗和手术计划的制定至关重要。针对目前基于单模态图像的肺部肿瘤区域分割的精度较低等问题,综合PET和CT图像的优缺点,提出一种全新的多模态肺部肿瘤图像分割方法。首先,使用区域生长法和数学形态学法对PET图像进行预分割以获取初始轮廓,初始轮廓用于获取PET图像和CT图像上随机游走所需的种子点,同时作为约束加入到CT图像的随机游走过程中;依据CT图像解剖特征较强的特点,利用CT解剖特征改进PET图像上随机游走的权值;最终将PET图像和CT图像上随机游走所获得的相似度矩阵进行加权,在PET图像和CT图像上获得一个相同的分割轮廓。实验表明,相较于其他传统分割算法,所提方法在肺部肿瘤区域分割上具有更高的精确度和更好的稳定性。

Accurate lung tumor segmentation is critical to the development of radiotherapy and surgical procedures.This paper proposed a new multimodal lung tumor image segmentation method by combining the advantages and disadvantages of PET and CT to solve the weakness of single-mode image segmentation,such as the unsatisfied segmentation accuracy.Firstly,the initial contour is obtained by the pre-segmentation of PET image through using region growing and mathematical morphology.The initial contour can be used to automatically obtain the seed points required for random walk of PET and CT images,at the same time,it can be also used as a constraint in the random walk of CT image to solve the shortcoming that the tumor area is not obvious if the CT image has not been enhanced.For the reason that CT provides essential details on anatomic structures,the anatomic structures of CT can be used to improve the weight of random walk on PET images.Finally,the similarity matrices obtained by random walk on PET and CT image are weighted to obtain an identical result on PET and CT images.Clinical PET-CT image segmentation of lung tumor shows that the proposed method has better performance than other traditional image segmentation methods.