Adaptive trimmed mean filter for computed tomographic imaging
Jiang Hsieh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The image quality of a computed tomography (CT) scan is frequently degraded by severe streaking artifacts resulting from excessive x-ray quantum noise. When this occurs, a patient has to be re-scanned at a higher x-ray technique to obtain an acceptable image for diagnosis. This approach results in not only unnecessary dosage to the patient, but also a delayed patient diagnosis and a reduced patient throughput. In this paper, we propose an adaptive trimmed mean filter (ATMF) in Radon space to combat this problem. The ATMF is an extension to the existing (alpha) -inner mean filter in that both sample size M and the trimming parameter (alpha) are selected based on the local statistics. In addition, the 2D ATMF is unsymmetrical to adapt to the sampling pattern in Radon pace. Phantom studies and clinical evaluations have shown that this type of filter is very effective in reducing or eliminating quantum noise induced artifacts. At the same time, the impact on the image spatial resolution has been kept to a minimum.