Locally Weighted Multi-atlas Construction
In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If atlases are regarded as representative patterns for a population of images, then multiple atlases are required for a heterogeneous population. In conventional atlas construction methods, the “unit” of representative patterns is images. Every input image is associated with its most similar atlas. As the number of subjects increases, the heterogeneity increases accordingly, and a big number of atlases may be needed. In this paper, we explore using region-wise, instead of image-wise, patterns to represent a population. Different parts of an input image is fuzzily associated with different atlases according to voxel-level association weights. In this way, regional structure patterns from different atlases can be combined together. Based on this model, we design a variational framework for multi-atlas construction. In the application to two T1-weighted MRI data sets, the method shows promising performance, in comparison with a conventional unbiased atlas construction method.
KeywordsInput Image Template Image Variational Framework Representative Pattern Warped Image
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