Acta Optica Sinica, Volume. 38, Issue 10, 1017001(2018)
Denoising Algorithm of Cerenkov Luminescence Images Based on Spatial Information Improved Clustering
Fig. 2. CLI images in numerical simulation. (a) Digital mouse, CL source location is the red point in blue circle; (b) simulated CLI image; (c) simulated CLI image after adding noises
Fig. 3. Denoising results of median filter algorithm with sliding window sizes of (a) 3, (b) 5, and (c) 7, as well as (d) denoising result of FLICMTV algorithm
Fig. 4. RMSE and SSIM of median filter and FLICMTV denoising algorithms. (a) RMSE of ROI; (b) SSIM of ROI; (c) RMSE; (b) SSIM
Fig. 5. Results of physical phantom experiment. (a) Original CLI image; (b) denoised CLI image with FLICMTV algorithm; (c) denoised CLI image with median filter algorithm with sliding window size of 5; (d) pixel intensity at red lines; (e) SSIM of different denoising algorithms in yellow rectangle
Fig. 6. Results of in vivo experiment. (a) White-light image of mouse, pseudotumor area is outlined in red circle; (b) original CLI image; (c) denoised CLI image with FLICMTV algorithm; (d) denoised CLI image with median filter algorithm with sliding window size of 5; (e) RMSE and (f) SSIM (red circle and all picture) for median filter and FLICMTV algorithms; (g) mean pixel intensity of ROI in fig. 6 (b), (c), and (d), respectively
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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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