Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Image Sequence Fusion Using a Shift-Invariant Wavelet Transform
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
IEEE Transactions on Neural Networks
Physiologically motivated image fusion for object detection using a pulse coupled neural network
IEEE Transactions on Neural Networks
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Image fusion using self-constraint pulse-coupled neural network
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Biological image fusion using a NSCT based variable-weight method
Information Fusion
Computer Methods and Programs in Biomedicine
Human visual system inspired multi-modal medical image fusion framework
Expert Systems with Applications: An International Journal
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
Spiking cortical model for multifocus image fusion
Neurocomputing
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Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis, and treatment planning. Pulse coupled neural network (PCNN) is derived from the synchronous neuronal burst phenomena in the cat visual cortex. However, it is very difficult to directly apply original PCNN into the field of image fusion, because its model has some shortcomings. Although a significant amount of research work has been done in developing various medical image algorithms, one disadvantage with the approaches is that they cannot deal with different kinds of medical images. In this instance, we propose a novel multi-channel model -m-PCNN for the first time and apply it to medical image fusion. In the paper, firstly the mathematical model of m-PCNN is described, and then dual-channel model as a special case of m-PCNN is introduced in detail. In order to show that the m-PCNN can deal with multimodal medical images, we used four pairs of medical images with different modalities as our experimental subjects. At the same time, in comparison with other methods (Contrast pyramid, FSD pyramid, Gradient pyramid, Laplacian pyramid, etc.), the performance and relative importance of various methods is investigated using the Mutual Information criteria. Experimental results show our method outperforms other methods, in both visual effect and objective evaluation criteria.