Accurate and robust image registration based on radial basis neural networks

Neural network-based image registration using global image features is relatively a new research subject, and the schemes devised so far use a feedforward neural network to find the geometrical transformation parameters. In this work, we propose to use a radial basis function neural network instead of feedforward neural network to overcome lengthy pre-registration training stage. This modification has been tested on the neural network-based registration approach using discrete cosine transformation features in the presence of noise. The experimental registration work is conducted in two different levels: estimation of transformation parameters from a local range for fine registration and from a medium range for coarse registration. For both levels, the performances of the feedforward neural network-based and radial basis function neural network-based schemes have been obtained and compared to each other. The proposed scheme does not only speed up the training stage enormously but also increases the accuracy and gives robust results in the presence of additive Gaussian noise owing to the better generalization ability of the radial basis function neural networks.