Medical image fusion based on hybrid intelligence

Abstract

Medical image fusion combines complementary images from different modalities for proper diagnosis and surgical planning. A new approach for medical image fusion based on the hybrid intelligence system is proposed. This paper has integrated the swarm intelligence and neural network to achieve a better fused output. The edges are an important feature of an image and they are detected and optimized by using ant colony optimization. The detected edges are enhanced and it is given as the feeding input to the simplified pulse coupled neural network. The firing maps are generated and the maximum fusion rule is applied to get the fused image. The performance of the proposed method is compared both subjectively and objectively, with the genetic algorithm method, neuro-fuzzy method and also with the modified pulse coupled neural network. The results show that the proposed hybrid intelligent method performs better when compared to the existing computational and hybrid intelligent methods.