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

• Published in: Journal of innovative optical health science Vol. 11; no. 4; pp. 1850017-1 - 1850017-8
• Main Authors: Cao, Xin, Sun, Yi, Kang, Fei, Wang, Lin, Yi, Huangjian, Zhao, Fengjun, Su, Linzhi, He, Xiaowei
• Format: Journal Article
• Language: English
• Published: World Scientific Publishing Company 01.07.2018; World Scientific Publishing
• Subjects: Cerenkov luminescence imaging; image processing; radionuclide imaging; Research Article; image processing; Cerenkov luminescence imaging; radionuclide imaging
• ISSN: 1793-5458; 1793-7205
• DOI: 10.1142/S1793545818500177

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.