Automatic process for processing medical images of a face for recognition of facial dysmorphisms, comprising the steps of: - acquiring in a first direction a plurality of two-dimensional planar medical images of a person's face, said images comprising a plurality of voxels; - segmenting said images so as to automatically obtain a three-dimensional output surface representative of a three-dimensional model of said face; - analyzing said three-dimensional output surface by using differential geometry methods, so as to automatically find and locate characteristic points common to the faces of all individuals; - classifying said facial images on the basis of geometric features of said characteristic points wherein the step of segmenting the images comprises the steps of: - filtering the images to remove any noise from said images; - extrapolating the facial surface by detecting, for each image, the face edges; - repeating the extrapolation step for images acquired in two other directions orthogonal to the first direction; - combining the extrapolated surfaces to obtain a combined set of images, wherein each voxel takes a binary value; - reconstructing the extrapolated surfaces to obtain said three-dimensional output surface, and wherein the step of extrapolating the facial surface comprises the steps of: - processing the whole plurality of images by applying a cubical kernel defined as follows: S ∨ l = { i j k ∈ C ∨ l: i − r = j − c ∧ k = s ∨ i − r = k − s ∧ j = c ∨ ∨ j − c = k − s ∧ i = r ∨ i − r = j − c = k − s ∨ ∨ i − r + j − c + k − s = max i − r j − c k − s} C ∨ l = r − l, r + l × c − l, c + l × s − l, s + l where v = ( r, c, s) is a generic voxel of the images and l is a predetermined parameter representative of the length of half the side of the kernel, so as to extract a subset of voxels; - computing the histogram of the grayscale of the kernel; - setting, for each voxel of said subset of voxels, a threshold value equal to a predetermined percentile of said grayscale histogram, thereby obtaining an intermediate set of images, wherein to each voxel of said subset of voxels an intensity value is assigned which is equal to the local threshold value; - setting, for each image, all the local threshold values of the voxels of the subset of voxels lower than a predetermined value (t) equal to zero, and computing the histogram of the grayscale of each image; - setting, for each image, a predetermined global threshold value; - mapping, for each image, the voxels of said subset of voxels belonging to the image into a binary output image wherein each voxel is equal to either TRUE or FALSE depending on the comparison between the global threshold value and the intensity value of the voxel; - determining the edges of the face by comparing the values of said voxels. - acquiring in a first direction a plurality of two-dimensional planar medical images of a person's face, said images comprising a plurality of voxels; - segmenting said images so as to automatically obtain a three-dimensional output surface representative of a three-dimensional model of said face; - analyzing said three-dimensional output surface by using differential geometry methods, so as to automatically find and locate characteristic points common to the faces of all individuals; - classifying said facial images on the basis of geometric features of said characteristic points - filtering the images to remove any noise from said images; - extrapolating the facial surface by detecting, for each image, the face edges; - repeating the extrapolation step for images acquired in two other directions orthogonal to the first direction; - combining the extrapolated surfaces to obtain a combined set of images, wherein each voxel takes a binary value; - reconstructing the extrapolated surfaces to obtain said three-dimensional output surface, and wherein the step of extrapolating the facial surface comprises the steps of: - processing the whole plurality of images by applying a cubical kernel defined as follows: S ∨ l = { i j k ∈ C ∨ l: i − r = j − c ∧ k = s ∨ i − r = k − s ∧ j = c ∨ ∨ j − c = k − s ∧ i = r ∨ i − r = j − c = k − s ∨ ∨ i − r + j − c + k − s = max i − r j − c k − s} C ∨ l = r − l, r + l × c − l, c + l × s − l, s + l where v = ( r, c, s) is a generic voxel of the images and l is a predetermined parameter representative of the length of half the side of the kernel, so as to extract a subset of voxels; - computing the histogram of the grayscale of the kernel; - setting, for each voxel of said subset of voxels, a threshold value equal to a predetermined percentile of said grayscale histogram, thereby obtaining an intermediate set of images, wherein to each voxel of said subset of voxels an intensity value is assigned which is equal to the local threshold value; - setting, for each image, all the local threshold values of the voxels of the subset of voxels lower than a predetermined value (t) equal to zero, and computing the histogram of the grayscale of each image; - setting, for each image, a predetermined global threshold value; - mapping, for each image, the voxels of said subset of voxels belonging to the image into a binary output image wherein each voxel is equal to either TRUE or FALSE depending on the comparison between the global threshold value and the intensity value of the voxel; - determining the edges of the face by comparing the values of said voxels.