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We propose a novel pairwise distance measure between image keypoint sets, forthe purpose of large-scale medical image indexing. Our measure generalizes theJaccard index to account for soft set equivalence (SSE) between keypointelements, via an adaptive kernel framework modeling uncertainty in keypointappearance and geometry. A new kernel is proposed to quantify the variabilityof keypoint geometry in location and scale. Our distance measure may beestimated between $O(N^2)$ image pairs in $O(N~\log~N)$ operations via keypointindexing. Experiments report the first results for the task of predictingfamily relationships from medical images, using 1010 T1-weighted MRI brainvolumes of 434 families including monozygotic and dizygotic twins, siblings andhalf-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalenceand the keypoint geometry kernel improve upon standard hard set equivalence(HSE) and appearance kernels alone in predicting family relationships.Monozygotic twin identification is near 100%, and three subjects with uncertaingenotyping are automatically paired with their self-reported families, thefirst reported practical application of image-based family identification. Ourdistance measure can also be used to predict group categories, sex is predictedwith an AUC=0.97. Software is provided for efficient fine-grained curation oflarge, generic image datasets.

Efficient Pairwise Neuroimage Analysis using the Soft Jaccard Index and 3D Keypoint Sets