A diagnostic imaging system which automatically corrects metal artifacts in an uncorrected tomographic image caused by high attenuating objects, the system comprising: a filter means for filtering the uncorrected tomographic image and which is adapted to reduce noise, characterized by: a means for clustering pixels of the filtered uncorrected tomographic image, which clustering means includes: a means for classifying pixels of the filtered uncorrected reconstructed image into at least metal, bone, tissue, and air pixel classes to generate a classified image (T class); a means for replacing metal class pixels of the classified image with pixel values of at least another pixel class to generate a metal free classified image (T class_final); a morphological means adapted to refine class regions of the metal free classified image (T class_final) using prior knowledge; a means for forward projecting the metal free classified image to generate a model projection data (S model); a means for identifying corrupted regions of original projection data contributing to the pixels of the metal class; and a means for replacing the identified corrupted regions with corresponding regions of the model projection data (S model) to generate corrected projection data (S') which is reconstructed by a reconstruction means into a corrected reconstructed image (T'). a filter means for filtering the uncorrected tomographic image and which is adapted to reduce noise, characterized by: a means for clustering pixels of the filtered uncorrected tomographic image, which clustering means includes: a means for classifying pixels of the filtered uncorrected reconstructed image into at least metal, bone, tissue, and air pixel classes to generate a classified image (T class); a means for replacing metal class pixels of the classified image with pixel values of at least another pixel class to generate a metal free classified image (T class_final); a morphological means adapted to refine class regions of the metal free classified image (T class_final) using prior knowledge; a means for forward projecting the metal free classified image to generate a model projection data (S model); a means for identifying corrupted regions of original projection data contributing to the pixels of the metal class; and a means for replacing the identified corrupted regions with corresponding regions of the model projection data (S model) to generate corrected projection data (S') which is reconstructed by a reconstruction means into a corrected reconstructed image (T'). a means for clustering pixels of the filtered uncorrected tomographic image, which clustering means includes: a means for classifying pixels of the filtered uncorrected reconstructed image into at least metal, bone, tissue, and air pixel classes to generate a classified image (T class); a means for classifying pixels of the filtered uncorrected reconstructed image into at least metal, bone, tissue, and air pixel classes to generate a classified image (T class); a means for replacing metal class pixels of the classified image with pixel values of at least another pixel class to generate a metal free classified image (T class_final); a morphological means adapted to refine class regions of the metal free classified image (T class_final) using prior knowledge; a means for forward projecting the metal free classified image to generate a model projection data (S model); a means for identifying corrupted regions of original projection data contributing to the pixels of the metal class; and a means for replacing the identified corrupted regions with corresponding regions of the model projection data (S model) to generate corrected projection data (S') which is reconstructed by a reconstruction means into a corrected reconstructed image (T').