Abstract: 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 tumorshows that the proposed method has better performance than other traditional image segmentation methods.

Key words: Image segmentation, Multimodal medical image, PET-CT, Random walk