DMCA
Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations
| Citations: | 8 - 0 self |
BibTeX
@MISC{Rohlfing_informationfusion,
author = {T. Rohlfing and A. Pfefferbaum and E. V. Sullivan and C. R. Maurer},
title = {Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations},
year = {}
}
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Abstract
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. 1
Keyphrases
information fusion biomedical image analysis data v multiple classifier system pattern recognition application atlas-based image segmentation image segmentation average image tissue classification successful tool deformation-based group morphometry classifier input different biomedical image analysis application computer-aided diagnosis multi-spectral classification
