A generalized family of fixed-radius distribution-based distance measures for content-based fMRI image retrieval

We present a family of distance measures for comparing activation patterns captured in fMRI images. We model an fMRI image as a spatial object with varying density, and measure the distance between two fMRI images using a novel fixed-radius, distribution-based Earth Mover's Distance that is computable in polynomial time. We also present two simplified formulations for the distance computation whose complexity is better than linear programming. The algorithms are robust in the presence of noise, and by varying the radius of the distance measures, can tolerate different degrees of within-class deformation. Empirical evaluation of the algorithms on a dataset of 430 fMRI images in a content-based image retrieval application demonstrates the power and robustness of the distance measures.