Exploiting Quality-Guided Adaptive Optimization for Fusing Multimodal Medical Images

Abstract

Multimodal medical image fusion (MMIF) plays critical roles in image-guided clinical diagnostics and treatment. Pulse coupled neural network (PCNN) has been applied in image fusion for several years. In the schemes of image fusion based on PCNN, the authors have adjusted variables manually, so that it is difficult to get satisfying effects which limit in dealing with medical images with different modalities. This paper presents a quality-guided adaptive optimization method for MMIF, which is based on PCNN optimized by multi-swarm fruit fly optimization algorithm (MFOA). To reduce the implementation cost and improve the performance of the MFOA, quality assessment for multimodal medical image fusion was chosen to be the hybrid fitness function. Guided by such quality measurement, the adaptive PCNN using the MFOA (PCNN-MFOA) is proposed, which could automatically fit the optimal variables to the source images and enhance the fusion effect. The experimental results visually and quantitatively show that the proposed fusion strategy is more effective than the state-of-the-art methods and it is more effective in processing medical images with different modalities.

Keywords

• Quality guided
• pulse coupled neural network
• multi-swarm fruit fly optimization algorithm
• multimodal medical image fusion