Medical image registration based on self-adapting pulse-coupled neural networks and mutual information. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Wang, Guanying; Xu, Xinzheng; Jiang, Xiangying; Ding, Shifei
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 1917
• Page End: 1926
• Publication Date: 2016-10
• Subjects: Medical image registration -- Pulse-coupled neural networks (PCNN) -- Self-adapting -- Mutual information; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1985-x
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Medical image registration plays a dominant role in medical image analysis and clinical research. In this paper, we present a new coarse-to-fine method based on pulse-coupled neural networks (PCNNs) and mutual information (MI). In the coarse-registration process, we use the PCNN-clusters' invariant characteristics of translation, rotation and distortion to get the coarse parameters. And the parameters of the PCNN model are optimized by ant colony optimization algorithm. In the fine-registration process, the coarse parameters provide a near-optimal initial solution. Based on this, the fine-tuning process is implemented by mutual information using the particle swarm optimization algorithm to search the optimal parameters. For the purpose of proving the proposed method can deal with medical image registration automatically, the experiments are carried out on MR and CT images. The comparative experiments on MI-based and SIFT-based methods for medical image registration show that the proposed method achieves higher performance in accuracy.