In nature and real life application domains it is common to encounter varied or textured areas, therefore in many cases it is of greater interest to partition the image into similarly varied, as opposed to similarly homogeneous subregions. We propose a novel, variation-guided approach to SLIC clustering, that has a potential to provide a useful alternative to standard supervoxels due to it’s ability to retain local variation information. We evaluate the method on a longitudinal DCE-MRI dataset of 10 mice scanned over 10 days. The method was able to produce contiguous segmentations, while significantly reducing computational complexity.
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