Fusion algorithm of medical images based on fuzzy logic

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.