Putting images on a manifold for atlas-based image segmentation
In medical image analysis, atlas-based segmentation has become a popular approach. Given a target image, how to select the atlases with the similar shape of anatomical structure to the input image is one of the most critical factors affecting the segmentation accuracy. In this paper, we propose a novel strategy by putting the images on a manifold to analyze the intrinsic similarity between the images. A subset of atlases can be selected and the optimal fusion weights are computed in a low-dimensional manifold space. Finally, it combines the selected atlases by using the corresponding weights for image segmentation. The experimental results demonstrated that our proposed method is robust and accurate especially when a large number of training samples are available.