针对图像处理的需求,提出一种基于双树复小波变换的图像去噪算法。该算法对图像进行双树复小波变换,对变换后的系数利用最大似然估计和维纳滤波进行去噪,采用最大后验概率来估计双树复小波的方差,利用维纳滤波得到去噪后的系数,通过双...针对图像处理的需求,提出一种基于双树复小波变换的图像去噪算法。该算法对图像进行双树复小波变换,对变换后的系数利用最大似然估计和维纳滤波进行去噪,采用最大后验概率来估计双树复小波的方差,利用维纳滤波得到去噪后的系数,通过双树复小波反变换得到去噪后的图像。在分解层计算方差时,均采用在4×4的结构元素内基于最大值和次大值坍缩后的邻域来计算方差。实验结果表明,该算法的PSNR对比其他文献提高0.2 d B左右,运行时间减少5 s。展开更多
Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical a...Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.展开更多
本研究简要概述了健康行为程式模型(health action process approach, HAPA)的形成、原则、理论假设和局限性,并探讨该模型在国内外不同人群和不同领域的应用现状,旨在为全民健康管理和健康行为的促进提供科学的、可行性高的行为预测理...本研究简要概述了健康行为程式模型(health action process approach, HAPA)的形成、原则、理论假设和局限性,并探讨该模型在国内外不同人群和不同领域的应用现状,旨在为全民健康管理和健康行为的促进提供科学的、可行性高的行为预测理论模型和干预指导框架。展开更多
文摘针对图像处理的需求,提出一种基于双树复小波变换的图像去噪算法。该算法对图像进行双树复小波变换,对变换后的系数利用最大似然估计和维纳滤波进行去噪,采用最大后验概率来估计双树复小波的方差,利用维纳滤波得到去噪后的系数,通过双树复小波反变换得到去噪后的图像。在分解层计算方差时,均采用在4×4的结构元素内基于最大值和次大值坍缩后的邻域来计算方差。实验结果表明,该算法的PSNR对比其他文献提高0.2 d B左右,运行时间减少5 s。
基金supported by grants from the National Key R&D Program of China(2017YFA0205200,2017YFC1308701,and 2017YFC1309100)National Natural Science Foundation of China(82001917,81930053,81227901,81771924,81501616,81571785,81771957,and 61671449)the Natural Science Foundation of Guangdong Province,China(2016A030311055 and 2016A030313770)。
文摘Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.
