Hybrid Methods of Contourlet Transform and Particle Swarm Optimization for Multimodal Medical Image Fusion

This paper presents the hybrid method of medical image fusion, and a hybrid method is a combination of PCNN and particle swarm optimization. The pulse coupled neural network is a dynamic neural network model, and the rate of recognition is very high. The particle swarm optimization is a swarm-based algorithm. The nature of the algorithm is iterative and reduces the unwanted noise factor during the process of fusion. For the extract of feature, coefficient applied contourlet transform, contourlet transform is rich feature extraction process in image processing. The method of contourlet transform is subband decomposition and directional feature coefficient with Laplacian pyramidal transform. The proposed algorithm increases the value of mutual information of the fused image. The experimental analysis process uses two types of images: computed tomography (CT) and magnetic resonance imaging (MRI). The proposed algorithm simulated in MATLAB software and tested with reputed medical image dataset. The process of evaluation measure standard parameters and compare with CTGA, and PCNN algorithm. Our experimental results show better performance instead of CTGA and PCNN.