A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion
| Autor: | Xin Cao, Yi Sun, Fei Kang, Lin Wang, Huangjian Yi, Fengjun Zhao, Linzhi Su, Xiaowei He |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Journal of Innovative Optical Health Sciences, Vol 11, Iss 4, Pp 1850017-1-1850017-8 (2018) |
| Druh dokumentu: | article |
| ISSN: | 1793-5458 1793-7205 17935458 |
| DOI: | 10.1142/S1793545818500177 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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