文献类型： 会议论文
第一作者： Sylvain Prima
作者： Sylvain Prima 1 ; Olivier Commowick 2 ;
作者机构： 1.INRIA, INSERM, VisAGeS U746 Unit/Project
2.University of Rennes I-CNRS UMR 6074
关键词： MRI;Brain;Bilateral symmetry;Denoising;NL-means
会议名称： IEEE International Symposium on Biomedical Imaging
主办单位：
页码： 1231-1234
摘要： The popular NL-means denoising algorithm proposes to modify the intensity of each voxel of an image by a weighted sum of the intensities of similar voxels. The success of the NL-means rests on the fact that there are typically enough such similar voxels in natural, and even medical images; in other words, that there is some self-similarity/redundancy in such images. However, similarity between voxels (or rather, between patches around them) is usually only assessed in a spatial neighbourhood of the voxel under study. As the human brain exhibits approximate bilateral symmetry, one could wonder whether a voxel in a brain image could be more accurately denoised using information from both ipsi- and contralateral hemispheres. This is the idea we investigate in this paper. We define and compute a mid-sagittal plane which best superposes the brain with itself when mirrored about the plane. Then we use this plane to double the size of the neigh-bourhoods and hopefully find additional interesting voxels to be included in the weighted sum. We evaluate this strategy using an extensive set of experiments on both simulated and real datasets.
分类号： tp391.41-53
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