An approach for 3D volumes matching

In 3D Computer Vision a relevant problem is to match a "Source" image dataset with a "Target" image dataset, that is to find the rule that controls the modification of the global characteristics of the Source in such a way as to match the Target. The matching problem can be faced using a neural net approach; where the nodes are related to the image voxels and the synapses to the voxel information, e.g. locations; grey values, gradients, angles. This paper presents the "volume-matcher 3D" project, an approach for a data-driven comparison and registration of three-dimensional images. The approach proposes a neural network model derived from the 'Self Organizing Maps' and extended in order to match a full 3D data set of a 'source volume: with the 3D data set of a 'target volume'. The algorithms developed have been tested on real cases of interest in medical imaging. The results have been evaluated on the basis of both the Mean Square Error and the visual analysis, performed by an expert, of the result volume. The software has been implemented on a high performance PC using AVS/Express (TM) software package for volume reconstruction; 'polytri' based algorithms have been used for this purpose.