Abstract: The presence of metal artifacts significantly compromises the clarity and clinical diagnostic value of computed tomography (CT) images, primarily manifesting as streaks and shadows that obscure key anatomical structures and impede the accurate assessment of pathological conditions by physicians. To address this issue, we proposes a novel CT image metal artifact reduction algorithm based on tissue processing and non-linear diffusion filtering. The algorithm initially employs multi-scale iterative threshold segmentation to precisely extract metal regions and generate metal projection trajectories, and subsequent wavelet denoising and interpolation of metal components in the projection domain contribute to producing preliminary corrected images. A tissue processing technique is then utilized to restore bone structure deficiencies caused by linear interpolation, whereas non-linear diffusion filtering is applied to suppress residual artifacts and effectively preserve edge details. Experimental results revealed that compared with conventional methods, the application of this algorithm facilitates a significant reduction in metal artifact intensity, prevents the generation of secondary artifacts, and maintains the integrity of bone and soft tissue structures surrounding metal implants, thereby enhancing CT image quality and clinical diagnostic utility.