Image matting is a method that separates foreground and background objects in an image, and has been widely used in medical image segmentation. Previous work has shown that matting can be formulated as a graph Laplacian matrix. In this paper, we derived matting from a local regression and global alignment view, as an attempt to provide a more intuitive solution to the segmentation problem. In addition, we improved the matting algorithm by adding a weight extension and refer to the proposed approach as Adaptive Weight Matting (AWM), where an adaptive weight was added to each local regression term to reduce the bias caused by outliers. We compared the segmentation results generated by the proposed method and several state-of-the-art segmentation methods, including conventional matting, graph-cuts and random walker, on medical images of different organs acquired using different imaging modalities. Experimental results demonstrated the advantages of AWM on medical image segmentation.