An efficient medical image super resolution based on piecewise linear regression strategy using domain transform filtering

2021 ◽  
Author(s):  
Dawa Chyophel Lepcha ◽  
Bhawna Goyal ◽  
Ayush Dogra ◽  
Shui‐Hua Wang
Keyword(s):  
Linear Regression ◽  
Medical Image ◽  
Piecewise Linear ◽  
Super Resolution ◽  
Piecewise Linear Regression ◽  
Image Super Resolution

Identifying changes in trends in the age standardized incidence of melanoma in Australia

2019 ◽  
Vol 3 (4) ◽  
pp. 250-252 ◽  
Author(s):  
David M Hille
Keyword(s):  
Public Health ◽  
Linear Regression ◽  
Iterative Process ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Health Issue ◽  
Public Health Issue ◽  
Piecewise Linear Regression ◽  
Significant Public Health ◽  
Males And Females

ObjectiveTo identify changes in the linear trend of the age-standardized incidence of melanoma in Australia for all persons, males, and females. MethodsA two-piece piecewise linear regression was fitted to the data. The piecewise breakpoint varied through an iterative process to determine the model that best fits the data.ResultsStatistically significant changes in the trendof the age-standardized incidence of melanoma in Australia were found for all persons, males, and females. The optimal breakpoint for all persons and males was at 1998. For females, the optimal breakpoint was at 2005. The trend after these breakpoints was flatter than prior to the breakpoints, but still positive.ConclusionMelanoma is a significant public health issue in Australia. Overall incidence continues to increase. However, the rate at which the incidence is increasing appears to be decreasing.

scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

U-Shaped Association Between the Triglycerides-Glucose index and the Risk of Incident Diabetes in Apparently Healthy Population: A Population-based Cohort Study

2020 ◽  
Author(s):  
Xiuping Xuan ◽  
Masahide Hamaguchi ◽  
Qiuli Cao ◽  
Okamura Takuro ◽  
Yoshitaka Hashimoto ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Incident Diabetes ◽  
Smoothing Function ◽  
Healthy Population ◽  
Piecewise Linear Regression ◽  
Tyg Index ◽  
Apparently Healthy

Abstract Background Although the triglycerides-glucose (TyG) index was thought to be a practical predictor of incident diabetes, the association between them has not been well characterized. The study aimed to further examine the association between the TyG index and incident diabetes in Japanese adults. Methods The cases were extracted of the individual participating in the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) study at Murakami Memorial Hospital from 2004 to 2015, and 14297individuals apparently healthy at baseline were included in the study. Cox proportional hazards models were used to evaluate the associations between baseline TyG levels and incident of T2DM, and a two-piecewise linear regression model was use to examine the threshold effect of the baseline TyG on incident diabetes using a smoothing function. The threshold level (i.e., turning point) was determined using trial and error. A log likelihood ratio test was also conducted to compare the one-line linear regression model with a two-piecewise linear model. Results During a median follow-up period of 5.26 (women) and 5.88 (men) years, 47 women and 182 men developed Type 2 diabetes. The risk of diabetes was strongly associated with the baseline TyG index in the fully adjusted model in men but not in women, and no dose-dependent positive relationship between incident diabetes and TyG was observed across TyG tertiles. Intriguingly, two-piecewise linear regression analysis showed a U-shaped association between the TyG index and incident T2DM. The risk of incident diabetes decreased by around 90% in women with TyG < 7.27 (HR: 0.09; P = 0.0435) and 80% in men with TyG < 7.97 (HR 0.21, P = 0.002) with each increment of the TyG index after adjusting for confounders. In contrast, the risk of incident T2DM significantly elevated with the increase in TyG index in men with TyG > 7.97 (HR: 2.42, P < 0.001) and women with TyG > 7.29 (HR 2.76, P = 0.0166). Conclusions A U-shaped association was observed between the TyG index and incident T2DM among healthy individuals, with the TyG threshold of 7.97 in men and 7.27 in women. This information may be useful for reducing incident diabetes by maintaining the TyG index near these thresholds.

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

2020 ◽  
Vol 57 (2) ◽  
pp. 021014
Author(s):  
刘可文 Liu Kewen ◽  
马圆 Ma Yuan ◽  
熊红霞 Xiong Hongxia ◽  
严泽军 Yan Zejun ◽  
周志军 Zhou Zhijun ◽  
...  
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Attention Network ◽  
Image Super Resolution

scholarly journals Piecewise Linear Regression Based on Plane Clustering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29845-29855 ◽  
Author(s):  
Xubing Yang ◽  
Hongxin Yang ◽  
Fuquan Zhang ◽  
Li Zhang ◽  
Xijian Fan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Piecewise Linear ◽  
Piecewise Linear Regression
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