Matching of medical images by self-organizing neural networks

A general approach to the problem of image matching which exploits a multi-scale representation of local image structure and the principles of self-organizing neural networks is introduced. The problem considered is relevant in many imaging applications and has been largely investigated in medical imagery, especially as regards the integration of different imaging procedures. A given pair of images to be matched, named target and stimulus respectively, are represented by Gabor Wavelets. Correspondence is computed by exploiting the learning procedure of a neural network derived from Kohonen's SOM. The SOM units coincide with the pixels of the target image and their weight are pointers to those of the stimulus images. The standard SOM rule is modified so as to account for image features. The properties of our method are tested by experiments performed on synthetic images. The considered implementation has shown that is able to recover a wide range of transformations including global affine transformations and local distortions. Tests in the presence of additive noise indicate considerable robustness against statistical variability. Applications to clinical images are presented.