One of the basic assumptions in the computed tomography (CT) is that the scanned object has constant attenuation characteristics across the thickness of the slice. In reality, however, this assumption is often violated. The projection data set of a head CT scan, for example, is often corrupted by bony structures partially intruded into the scanning plane. As a result, severe streaking and shading artifacts appear in the reconstructed images. This phenomenon is called partial volume. In this paper, we propose an iterative approach to the partial volume artifact reduction. CT images are first reconstructed with a filtered backprojection algorithm. The generated images subsequently undergo a fuzzy membership classification process to arrive at bone-only images, which in turn will be used to produce gradient images. The projection error is then calculated based on the gradient image. For better error estimation, the scan data is collected in a helical mode and highly overlapped images are reconstructed. The error term is filtered and back-projected to produce a partial volume error image, which is scaled and subtracted from the original image. Various phantom studies have demonstrated the effectiveness of our approach.