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

Authors:
John Novatnack;Nicu Cornea;Ali Shokoufandeh;Deborah Silver;Sven Dickinson;Paul Kantor;Bing Bai
Affiliations:
Department of Computer Science, Drexel University, 3141 Chestnut Str., Philadelphia, PA, United States;Department of Electrical and Computer Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ, United States;Department of Computer Science, Drexel University, 3141 Chestnut Str., Philadelphia, PA, United States;Department of Electrical and Computer Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ, United States;Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, ON, Canada;Department of Library and Information Science, Rutgers University, 4 Huntington Str., New Brunswick, NJ, United States;Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ, United States
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

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.