In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid...In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid two-phase flow patterns in vertical and inclined 30° upward pipes with the testing ranges of superficial water velocity 0.02—0.4 m·s-1 and superficial gas velocity 0.005—2.7 m·s-1.The gray level co-occurrence matrix(GLCM)was used to quantitatively characterize 2D information in the local neighborhood of image for analyzing flow pattern image features and the four time-varying characteristic parameter indices which represented image texture structures of different flow patterns were extracted.Then the transition of flow structure in the development process of flow patterns and calculated Lempel-Ziv sequence complexity of the four time-varying characteristic parameter indices were analyzed,and compared with the complexity measurement,fractal scale and recurrence plot determinism calculated by conductance fluctuating signals.The study showed that the dynamic parameter evolution trends of flow pattern image texture structure characteristics described the variation of different flow pattern structures and dynamics complexity,and the correlation index(COR)was more effective to reflect the complexity of flow pattern dynamics than others.It indicated that the proposed dynamic image analysis method was helpful to understanding the flow pattern temporal and spatial evolution characteristics and also was an effective approach to identifying the gas-liquid two-phase flow patterns.展开更多
很多研究都已经证实当人体的组织发生病理变化时,图像纹理特征也会相应改变,如何从MRI图像中提取表征这种常规MRI无法观察到的微观结构改变的特征是本文研究的重点。本文旨在开发一个对MRI医学图像进行纹理分析的系统,其功能包括实现图...很多研究都已经证实当人体的组织发生病理变化时,图像纹理特征也会相应改变,如何从MRI图像中提取表征这种常规MRI无法观察到的微观结构改变的特征是本文研究的重点。本文旨在开发一个对MRI医学图像进行纹理分析的系统,其功能包括实现图像的显示及图像信息的读取。选取ROI(Regions of Interest,感兴趣区),对感兴趣区提取5个纹理特征,形成样本,存入样本库,以及对样本进行分类和统计检验。本系统的开发为从纹理分析的角度研究MRI医学图像提供了方便,具有较高的实用价值。展开更多
文摘In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid two-phase flow patterns in vertical and inclined 30° upward pipes with the testing ranges of superficial water velocity 0.02—0.4 m·s-1 and superficial gas velocity 0.005—2.7 m·s-1.The gray level co-occurrence matrix(GLCM)was used to quantitatively characterize 2D information in the local neighborhood of image for analyzing flow pattern image features and the four time-varying characteristic parameter indices which represented image texture structures of different flow patterns were extracted.Then the transition of flow structure in the development process of flow patterns and calculated Lempel-Ziv sequence complexity of the four time-varying characteristic parameter indices were analyzed,and compared with the complexity measurement,fractal scale and recurrence plot determinism calculated by conductance fluctuating signals.The study showed that the dynamic parameter evolution trends of flow pattern image texture structure characteristics described the variation of different flow pattern structures and dynamics complexity,and the correlation index(COR)was more effective to reflect the complexity of flow pattern dynamics than others.It indicated that the proposed dynamic image analysis method was helpful to understanding the flow pattern temporal and spatial evolution characteristics and also was an effective approach to identifying the gas-liquid two-phase flow patterns.
文摘目的探讨MRI检查T2加权成像(T2-weighted imaging,T2WI)及弥散加权成像(diffusion-weighted imaging,DWI)图像纹理参数与宫颈鳞状细胞癌放化疗疗效的相关性。方法回顾性纳入2015年2月至2016年1月北京协和医院接受放化疗的宫颈鳞状细胞癌患者,并根据其预后分为疾病进展组和疾病稳定组。采用TexRAD软件对两组患者放化疗前T2WI、DWI序列图像进行纹理分析,得到空间尺度滤波器(spatial scale filter,SSF)半径值为2、4、6的图像纹理参数。比较两组患者图像纹理参数差异,采用多因素Cox回归分析图像纹理参数与宫颈鳞状细胞癌患者放化疗疗效的相关性。采用受试者工作特征(receiver operating characteristic,ROC)曲线分析各图像纹理参数预测宫颈鳞状细胞癌放化疗后疾病进展的性能。结果共121例符合纳入和排除标准的宫颈鳞状细胞癌患者入选本研究。其中疾病进展组46例,疾病稳定组75例。T2WI序列图像中,疾病进展组与疾病稳定组患者的图像纹理参数均值(SSF2、SSF4、SSF6)、偏度(SSF2、SSF4)、熵(SSF4、SSF6)均有显著性差异(P均<0.05);DWI序列图像中,疾病进展组与疾病稳定组患者的图像纹理参数均值(SSF2、SSF4、SSF6)、偏度(SSF4、SSF6)、峰度(SSF2、SSF4)均有显著性差异(P均<0.05)。多因素Cox回归分析结果显示,T2WI序列图像纹理参数均值(SSF2、SSF4、SSF6)及DWI序列图像纹理参数均值(SSF2、SSF6)、熵(SSF2、SSF4、SSF6)、偏度(SSF4、SSF6)与宫颈鳞状细胞癌放化疗疗效具有相关性(P<0.05)。ROC曲线分析结果显示,图像纹理参数均值(T2WI-SSF2、T2WI-SSF4、T2WI-SSF6、DWI-SSF2、DWISSF6)、偏度(DWI-SSF6)可预测宫颈鳞状细胞癌放化疗后的疾病进展,曲线下面积(area under the curve,AUC)为0.625~0.746。其中,均值(T2WI-SSF4)的预测效能最高(AUC:0.746),其次为均值(T2WI-SSF2,AUC:0.725)、均值(T2WI-SSF6,AUC:0.703)。结论基线MRI检查T2WI、DWI图像纹理参数与宫颈鳞状细胞癌放化疗疗效具有相关性,其均值、偏度可预测宫颈鳞状细胞癌放化疗后疾病进展,且以均值的预测效能最高。
文摘很多研究都已经证实当人体的组织发生病理变化时,图像纹理特征也会相应改变,如何从MRI图像中提取表征这种常规MRI无法观察到的微观结构改变的特征是本文研究的重点。本文旨在开发一个对MRI医学图像进行纹理分析的系统,其功能包括实现图像的显示及图像信息的读取。选取ROI(Regions of Interest,感兴趣区),对感兴趣区提取5个纹理特征,形成样本,存入样本库,以及对样本进行分类和统计检验。本系统的开发为从纹理分析的角度研究MRI医学图像提供了方便,具有较高的实用价值。
