Journal of Innovative Optical Health Sciences, 2018, 11 (4): 1850017, Published Online: Oct. 6, 2018  

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

Author Affiliations
1 School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, P. R. China
2 Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, P. R. China
Abstract
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.

Xin Cao, Yi Sun, Fei Kang, Lin Wang, Huangjian Yi, Fengjun Zhao, Linzhi Su, Xiaowei He. A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion[J]. Journal of Innovative Optical Health Sciences, 2018, 11(4): 1850017.

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