Label Image Constrained Multiatlas Selection

Multiatlas based method is commonly used in medical image segmentation. In multiatlas 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 prostate structures in raw images, it is difficult to get accurate atlas selection results by 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). This paper tries to address this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Analyzing the data point distribution of the selected atlases in the manifold subspace, a novel weight computation method for atlas combination is proposed. Compared with other related existing methods, the experimental results on prostate segmentation from T2w MRI showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.