Acta Optica Sinica, Volume. 38, Issue 10, 1017001(2018)
Denoising Algorithm of Cerenkov Luminescence Images Based on Spatial Information Improved Clustering
With a widely available clinical radionuclide probe, Cerenkov luminescence imaging becomes one of the hot research topics in the field of optical molecular imaging. However, a large number of pulse noises on Cerenkov luminescence image, which are produced during the decay of radionuclide, seriously affect the following researches based on Cerenkov luminescence images, such as quantitative analysis, 3D reconstruction and so on. To suppress these pulse noises, we propose a denoising algorithm based on fuzzy local information C-means clustering algorithm and total variation model. The numerical simulation experiment, physical phantom experiment and animal experiment demonstrate that compared to the common used median filter algorithm, the proposed algorithm can remove the impulse noised effectively with the ability of maintaining the shape of Cerenkov Luminescence source.
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Xiaowei He, Yi Sun, Xiao Wei, Di Lu, Xin Cao, Yuqing Hou. Denoising Algorithm of Cerenkov Luminescence Images Based on Spatial Information Improved Clustering[J]. Acta Optica Sinica, 2018, 38(10): 1017001
Category: Medical Optics and Biotechnology
Received: Mar. 29, 2018
Accepted: May. 7, 2018
Published Online: May. 9, 2019
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