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Fusion algorithm of medical images based on fuzzy logic

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4 Author(s)
Jionghua Teng ; School of Automation, Northwestern Polytechnical University, NPU, Xi'an, China ; Suhuan Wang ; Jingzhou Zhang ; Xue Wang

Computed tomography (CT) and nuclear magnetic resonance imaging (MRI) are complementary on reflecting human body information. In order to provide more useful information for clinical diagnosis, we have a need to fuse the effective information. In the pixel-level fusion between the medical images, we presented a Mamdani-type minimum-sum-mean of maximum (MIN-SUM-MOM) algorithm in this paper. In MIN-SUM-MOM algorithm, fuzzy implication operation utilized MIN algorithm; calculating the membership functions of total output fuzzy sets employed SUM algorithm; defuzzification operation used MOM algorithm. Employ the data of medical image CT and MRI to achieve the fusion simulation, and compare with the simulation results of minimum-maximum-Centroid (MIN-MAX-Centroid) algorithm on the basis of the evaluation standards which are the standard deviation and the information entropy. By the contrast and analysis, we got the following conclusions: the fused images based on MIN-SUM-MOM algorithm not only reserve more texture features, but also enhance the information characteristics of two original images.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on  (Volume:2 )

Date of Conference:

10-12 Aug. 2010