Although physical metrics can objectively characterize computed tomography (CT) image quality, quantitative approaches to predict human observer performance are more accurate and clinically relevant. This study compared a modified channelized Hotelling model observer (CHO) with human observers in a shape discrimination task. Eight lesion-mimicking rods (two contrasts, two sizes and two shapes) were inserted into a 35 × 26 cm(2) torso-shaped water phantom and scanned 100 times on a 128-slice CT scanner at five dose levels. CT images were reconstructed using filtered backprojection (FBP) and iterative reconstruction (IR) techniques. Two-alternative forced choice studies were constructed with hexagonal and circular rod images put side-by-side in a randomized order. An edge mask was introduced to CHO to reflect the human observers' emphasis on lesion boundaries in discriminating shape. For small size lesions, the performance of three human observers and the modified CHO was highly correlated across lesion contrasts, CT doses and reconstruction algorithms; while for large size lesions, a ceiling effect was observed for both human and model observers' performance at high doses. Our result suggests the potential of CHO to predict human observer performance for both FBP and IR. For this shape discrimination task with uniform background, IR significantly improved human and model observer performance compared to FBP, with the amount of improvement depending on lesion size, contrast and dose.