Rician Noise Removal in MR Images Using an Adaptive Trilateral Filter
Noise removal in MR images has been challenging due to the unpleasant noise with a Rician distribution, which is signal dependent. Denoising of MR images is of importance for subsequent diagnoses and analyses, such as tissue classification, segmentation, and registration. We propose a post-acquisition denoising algorithm to adequately and adaptively remove the random fluctuations and bias introduced by Rician noise. The proposed filter consists of geometric, radiometric, and median-metric components that replaces the intensity value with an weighted average between neighboring pixels associated with an entropy function. In addition, a parameter automation mechanism is proposed to reduce the burden of laborious interventions through a fuzzy membership function, which adaptively responses to local intensity difference. Quantitatively and qualitatively experimental results indicate that this new filter outperformed several existing methods in providing greater noise reduction and clearer structure boundaries in a variety of MR images.
Herng-Hua Chang Tung-Ju Hsieh Yun-Ni Ting Woei-Chyn Chu
Computational Biomedical Engineering Laboratory (CBEL) Department of Engineering Science and Ocean E Institute of Biomedical Engineering National Yang-Ming University, Beitou 112 Taipei, Taiwan Computational Biomedical Engineering Laboratory (CBEL) Department of Engineering Science and Ocean E
国际会议
上海
英文
466-470
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）