1. A method of automatic image registration comprising: receiving a first medical image and a second medical image, the first medical image and the second medical image being of objects and with at least part of one object being common to both the first medical image and the second medical image; for each of the first medical image and the second medical image, computing a probability map comprising, for each image element, a probability that the image element is of a specified object, computing the probability map comprising using a regression forest comprising a plurality of regression trees each having been trained to produce probability map information; finding a mapping to register the first medical image and the second medical image by optimizing an energy function which is a function of the intensities of the first medical image and second medical image and also of the probability maps, the energy function comprising: a summation of a term related to a Kullback-Leibler divergence; and a summation of a term related to a marginal entropy of the first medical image and a term related to a marginal entropy of the second medical image, less a term related to a joint entropy of the first medical image and the second medical image; and at least one of: displaying at least the first medical image as a contour map overlaid on top of at least the second medical image; or displaying at least the first medical image or the second medical image such that the color or transparency of the at least first medical image or the second medical image is related to an estimated uncertainty of a registration value.