论文部分内容阅读
A novel class of active contour models for image segmentation is developed recently.It makes use of nonlocal comparisons between pairs of patches within each region to be segmented.The corresponding variational segmentation problem is implemented using a level set formulation that can handle an arbitrary number of regions.The pairwise interaction of features constrains only the local homogeneity of image features,which is crucial in capturing regions with smoothly spatially varying features.This segmentation method is generic and can be adapted to various segmentation problems by designing an appropriate metric between patches.To reduce the manual labor time and improve the accuracy of liver tumor segmentation in the treatment planning of radiofrequency ablation(RFA),the proposed method is used for liver tumor image segmentation A multi Gabor feature map of the liver tumor image is computed to describe the homogeneity of patches in a nonlocal way,and the nonlocal comparisons between pairs of patches are used to calculate the active contour energy.The whole energy function is minimized via a level set method to give the final segmentation.The experimental results indicate that the proposed method leads to good liver tumor segmentation with a good robustness to initialization condition.