Robust Anatomical Landmark Detection with Application to MR Brain Image Registration
We propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. Experimental results show that our method is capable of providing good initialization of registration, even for the images with large deformation difference, thus improving registration accuracy.
landmark detection regression forest deformable registration mri.