In clinical reconstruction of CT images, an effective energy is used instead of total X-ray spectrum. This approximation causes an accuracy decline. To increase the contrast, single source or dual source dual energy CT can be used to reach optimal values of tissue differentiation. However, these infrastructures are still at the laboratory level. In this study, we covered this issue by proposing a post-processing approach for converting a total X-ray spectrum into irregular intervals of quantized energy. We simulate a phantom in GATE/GEANT4 and irradiate it based on CT configuration. Inverse Radon transform is applied to the acquired Sinogram to construct the Pixel-based Attenuation Matrix (PAM). To construct images represented by each interval, water attenuation coefficient of the interval is extracted from NIST and used in the Hounsfield unit (HU) scale in conjunction with PAM. The CT image is modified by using of an associated normalized photon flux and calculated HU corresponding to the interval. We demonstrate the proposed method efficiency via complexity analysis, using entropy measures, Kolmogorov complexity, morphological richness, and quantitative segmentation criteria associated with standard fuzzy C-means. It was observed that the irregularity of the modified CT images decreases over the simulated ones.