No abstract

We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any non-rigid registration is presented. Following recent developments in non-local image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.

This paper describes a novel approach to forming high resolution MR images of the human fetal brain. It addresses the key problem of fetal motion by proposing a registration refined compounding of multiple sets of orthogonal fast 2D MRI slices, that are currently acquired for clinical studies, into a single high resolution MRI volume.A robust multi-resolution slice alignment is applied iteratively to the data to correct motion of the fetus that occurs between 2D acquisitions. This is combined with an intensity correction step and a super resolution reconstruction step, to form a single high isotropic resolution volume of the fetal brain.Experimental validation on synthetic image data with known motion types and underlying anatomy, together with retrospective application to sets of clinical acquisitions, are included.Results indicate that this method promises a unique route to acquiring high resolution MRI of the fetal brain in vivo allowing comparable quality to that of neonatal MRI. Such data provides a highly valuable window into the process of normal and abnormal brain development which is directly applicable in a clinical setting.