17. A method of automatic medical 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 the first medical image and the second medical image; for each of the first and second medical images, computing a probability map comprising, for each image element, a probability that the image element is of a specified object; wherein computing the probability map comprises, for at least one specified object, computing a posterior distribution of the location of the specified object in each of the first and second medical images by using a regression forest comprising a plurality of regression trees each having been trained to predict a location of the specified object; finding a mapping to register the first and second medical images by optimizing an energy function which is a function of the intensities of the first and second medical images 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 and second medical images.
17. A method of automatic medical 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 the first medical image and the second medical image; for each of the first and second medical images, computing a probability map comprising, for each image element, a probability that the image element is of a specified object; wherein computing the probability map comprises, for at least one specified object, computing a posterior distribution of the location of the specified object in each of the first and second medical images by using a regression forest comprising a plurality of regression trees each having been trained to predict a location of the specified object; finding a mapping to register the first and second medical images by optimizing an energy function which is a function of the intensities of the first and second medical images 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 and second medical images. 18. The method as claimed in claim 17 , wherein the first and second medical images are three or higher dimensional volumetric images.