The main problem in processing the control portal images is their poor quality. We have developed a way of improving the image quality to allow a segmentation stage. Three items were studied for this purpose the background gradient of intensity correction, the noise reduction, and the image restoration. The background was corrected by subtracting a smoothed version of the image from the original. We tested 15 noise reduction methods. The most appropriate for control portal images was found to be the truncated average. Finally, four restoration techniques were compared. The maximum a posteriori (MAP) algorithm was the most efficient. The algorithms were tested over a wide range of conditions (image quality). They produced a great improvement in anatomic detail for all the imaging systems, energies, and anatomical zones tested. For example, the signal-to-noise ratio of a SRI-100 pelvis image, acquired with 4 monitor units (MU) at 10 MV (very low quality image), increased from 0.97 to 42.84 after preprocessing. We found that the improvement in image quality facilitated or even enabled segmentation of the control portal images. The percentage of segments belonging to a structure increased from 30% to 65% in the example cited. The preprocessing of control portal images is the first step in checking the patient setup.