Multi-atlas based method is commonly used in image segmentation. In multi-atlas based image segmentation, atlas selection and combination are considered as two key factors affecting the performance. Recently, manifold learning based atlas selection methods have emerged as very promising methods. However, due to the complexity of structures in raw images, it is difficult to get accurate atlas selection results only measuring the distance between raw images on the manifolds. Although the distance between the regions to be segmented across images can be readily obtained by the label images, it is infeasible to directly compute the distance between the test image (gray) and the label images (binary). Here is a small try to solve this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Compared with other related existing methods, the experimental results on prostate segmentation showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.

We present a multi-atlas-based framework for accurate, consistent and simultaneous segmentation of a group of target images. Multi-atlas-based segmentation algorithms consider concurrently complementary information from multiple atlases to produce optimal segmentation outcomes. When segmenting a group of target images, most current methods consider these images independently with disregard of their correlation, thus resulting in inconsistent segmentations of the same structures across different target images.