Multisource Signal Fusion using Dempster Shafer Evidence Accumulation Concept and its Applications to CMFD and Multimodal Biomedical Image Fusion

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Dipankar Ray
D Dutta Majumder, Amit Das


This paper addresses a soft computing approach of fusion of signals from different independent sources. The signals may be from
different types of primary classifiers. The Dempster Shafer Evidence Accumulation (DSEA) theory provides a robust platform for evidence
fusion and it incorporates uncertainty, imprecision and conflicting situations in the process of decision making into a mathematical framework.
Primarily, Neuro-Fuzzy classifiers have been used on the signals of each individual source to classify them into meaningful clusters and to
assign mass value to each cluster, then Dempster Shafer Evidence Accumulation engine (DSEAE) has been used to combine them for final
output with proper classification to different admissible clusters. We have cited two experimental results of the use of this concept. Firstly, the
concept has been studied on a diesel engine to fuse the coolant flow signals from three primary ANN classifiers; secondly, it has been used to
fuse SPECT and MR-T2 registered brain images classified by fuzzy C-means method.



Keywords: Dempster-Shafer combination rule, data fusion, Multimodal medical Image fusion, CMFD


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