There are unavoidably lots of noises in tablet images due to natural or man-made decay, which have a significant affect on learning and studying of the ancient Chinese calligraphy works with Chinese tablet images. To address this problem, an integrated denoising method, based on assemble of multiple image smoothing filters, is proposed in this paper. To avoid damaging characters and losing detail information, input Chinese tablet images are enhanced by the Guided filter and multi-scale Retinex filter firstly. Then the enhanced tablet images are converted to binary ones by the Otsu thresholding filter. Finally, most random and block noises are removed using an improved scan-length statistics filter based on connected region. The performance of the proposed method was validated on our Chinese tablet image data set, which consists of 200 Chinese tablet images with different kinds of noise. Experiments show that, the proposed method can effectively remove most image noise (including various block noise, linear noise and ant-like noise) and preserve characters better than existing methods.
Diabetic retinopathy, the most serious ocular complication of diabetes, imposes a serious economic burden on society. Automatic and objective assessment of vessel changes can effectively manage diabetic retinopathy and prevent blindness. Optical coherence tomography angiography (OCTA) metrics have been confirmed to be used to assess vessel changes. The accuracy and reliability of OCTA metrics are restricted by vessel segmentation methods. In this study, a multi-branch retinal vessel segmentation method is proposed, which is comparable to the segmentation results obtained from the manual segmentation, effectively extracting vessels in low contrast areas and improving the integrity of the extracted vessels. OCTA metrics based on the proposed segmentation method were validated to be reliable for further analysis of the relationship between OCTA metrics and diabetes and the severity of diabetic retinopathy. Changes in vessel morphology are influenced by systemic risk factors. However, there is a lack of analysis of the relationship between OCTA metrics and systemic risk factors. We conducted a cross-sectional study that included 362 eyes of 221 diabetic patients and 1,151 eyes of 587 healthy people. Eight systemic risk factors were confirmed to be closely related to diabetes. After controlling these systemic risk factors, significant OCTA metrics (such as vessel complexity index, vessel diameter index, and mean thickness of retinal nerve fiber layer centered in the macular) were found to be related to diabetic retinopathy and severe diabetic retinopathy. This study provides evidence to support the potential value of OCTA metrics as biomarkers of diabetic retinopathy.
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