We define a transfer function based on the first and second statistical moments. We consider the evolution of the mean and variance with respect to a growing neighborhood around a voxel. This evolution defines a curve in 3D for which we identify important trends and project it back to 2D. The resulting 2D projection can be brushed for easy and robust classification of materials and material borders. The transfer function is applied to both CT and MR data.
We present a novel transfer function specification that uses the first and second statistical moments - the mean and variance. With these basic measures we are able to classify materials based on nontrivial properties, such as a material’s internal variance. The transfer function is also robust to uniformly distributed noise and both identifies material boundaries and differentiates between them. The robustness of the method comes from the fact that it simultaneously considers the data on multiple scales when calculating the first and second statistical moments.