Prior knowledge enhanced random walk for lung tumor segmentation from low-contrast CT images

The separation of a lung tumor from adjacent normal tissue, which has similar intensity values and indistinct boundaries on low-contrast CT images is a challenging task. In this paper, a prior knowledge enhanced random walk (RW) is proposed to account for the prior functional knowledge from PET and intensity information from CT. The prior knowledge acquired from PET is used for the automated selection of foreground seeds, defined as the tumor confidence region, the background seeds and the walking range to increase computational efficiency of the RW algorithm in CT. The tumor confidence region is also used for balancing transition, and thus limiting the information propagation range through a weight factor. The experimental evaluation on 18 low-contrast CT images with manual tumor segmentation demonstrated that our method outperformed RW and random walk from restart (RWR) as measured by the Dice similarity coefficient (DSC).