A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion

作者：Cao, Xin; Sun, Yi; Kang, Fei; Wang, Lin; Yi, Huangjian; Zhao, Fengjun; Su, Linzhi^*; He, Xiaowei^*

来源：Journal of Innovative Optical Health Sciences, 2018, 11(4): 1850017.

DOI：10.1142/S1793545818500177

• Cerenkov luminescence imaging
• image processing
• radionuclide imaging

With widely availed clinically used radionuclides, Cerenkov luminescence imaging (CLI) has become a potential tool in the field of optical molecular imaging. However, the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly, which affects the accuracy of quantitative analysis, as well as the three-dimensional reconstruction. In this work, a novel denoising framework based on fuzzy clustering and curvature-driven diffusion (CDD) is proposed to remove this kind of impulse noises. To improve the accuracy, the Fuzzy Local Information C-Means algorithm, where spatial information is evolved, is used. We evaluate the performance of the proposed framework systematically with a series of experiments, and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method. We hope this work may provide a useful data pre-processing tool for CLI and its following studies.

• 出版日期2018-7
• 单位中国人民解放军空军军医大学; 西北大学; 第四军医大学