Information fusion in biomedical image analysis: combination of data vs. combination of interpretations

Authors:
T. Rohlfing;A. Pfefferbaum;E. V. Sullivan;C. R. Maurer
Affiliations:
Neuroscience Program, SRI International, Menlo Park, CA;Neuroscience Program, SRI International, Menlo Park, CA;Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA;Department of Neurosurgery, Stanford University, Stanford, CA
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

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Abstract

Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs rather than on their outputs, as is usually done. We introduce the distinction between combination of data (i.e., classifier inputs) vs. combination of interpretations (i.e., classifier outputs). We illustrate the two levels of information fusion using four different biomedical image analysis applications that can be implemented using fusion of either data or interpretations: atlas-based image segmentation, “average image” tissue classification, multi-spectral classification, and deformation-based group morphometry.