目的:探讨纹理分析在鉴别眼眶淋巴瘤与炎性假瘤中的价值。方法:纳入经手术病理证实的眼眶淋巴瘤25例和炎性假瘤24例,使用MaZda软件提取T 1WI对比增强(contrast-enhanced T 1-weighted images,CE-T 1W)肿瘤纹理特征。比较两组游程长矩阵(...目的:探讨纹理分析在鉴别眼眶淋巴瘤与炎性假瘤中的价值。方法:纳入经手术病理证实的眼眶淋巴瘤25例和炎性假瘤24例,使用MaZda软件提取T 1WI对比增强(contrast-enhanced T 1-weighted images,CE-T 1W)肿瘤纹理特征。比较两组游程长矩阵(run-length matrix,RLM)的20个纹理特征参数,对差异有统计意义的参数进行主成分分析(principal component analysis,PCA)。采用多变量logistic回归模型分别对在两组间差异有统计学意义的特征参数及主成分进行建模,绘制受试者工作特征曲线(receiver operating characteristic curve,ROC)以评价模型效能。结果:淋巴瘤组灰度不均匀度(grey level non-uniformity,GLNU)、长游程优势(long run emphasis,LRE)的4个参数高于炎性假瘤(P<0.001),短游程因子(short run emphasis,SRE)、游程图像分数(fraction of image in runs,FIR)的4个参数低于炎性假瘤组(P<0.001);游程长不均匀度(run length non-uniformity,RLNU)的4个参数在两组间差异无统计学意义(P>0.05)。在两组间差异有统计学意义的参数中,以GLNU的参数建立的多变量logistic回归模型的诊断效能最高,灵敏度、特异度及曲线下面积(area under curve,AUC)分别为95.9%、72.0%、0.905;PCA提取出的2个主成分建立的多变量logistic回归模型的灵敏度、特异度及AUC分别为87.5%、88.0%、0.913。结论:基于CE-T 1W的RLM特征可有效鉴别眼眶淋巴瘤与炎性假瘤,其中以GLNU的诊断效能最高。展开更多
Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and m...Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the effi cient development and utilization of deep CBM.展开更多
文摘目的灰度游程长矩阵参数可以反映图像纹理的粗细及均匀程度。本研究采用该方法研究阿尔茨海默病(Alzheimer's disease,AD)患者脑MR图像中胼胝体的三维纹理特征,以反映AD患者胼胝体部位的病理变化,从而探索该纹理特征分析方法在该疾病诊断中的应用。方法选取18例AD患者及18例健康对照者,采用灰度游程长矩阵提取每位受试者胼胝体部位的4个三维纹理特征参数:短游程因子、长游程因子、灰度不均匀度和游程长不均匀度。比较两组间各纹理特征的差异,并分析这些纹理参数与临床广泛应用的简易智能状态检查量表(mini-mental state examination,MMSE)评分之间的相关性。结果 4个纹理参数两组相比较均有显著性差异,且与MMSE评分均具有相关性。结论基于三维纹理特征的灰度游程分析法能在一定程度上反映出AD患者胼胝体部位的病理变化,可能用于该疾病的临床诊断。
文摘目的:探讨纹理分析在鉴别眼眶淋巴瘤与炎性假瘤中的价值。方法:纳入经手术病理证实的眼眶淋巴瘤25例和炎性假瘤24例,使用MaZda软件提取T 1WI对比增强(contrast-enhanced T 1-weighted images,CE-T 1W)肿瘤纹理特征。比较两组游程长矩阵(run-length matrix,RLM)的20个纹理特征参数,对差异有统计意义的参数进行主成分分析(principal component analysis,PCA)。采用多变量logistic回归模型分别对在两组间差异有统计学意义的特征参数及主成分进行建模,绘制受试者工作特征曲线(receiver operating characteristic curve,ROC)以评价模型效能。结果:淋巴瘤组灰度不均匀度(grey level non-uniformity,GLNU)、长游程优势(long run emphasis,LRE)的4个参数高于炎性假瘤(P<0.001),短游程因子(short run emphasis,SRE)、游程图像分数(fraction of image in runs,FIR)的4个参数低于炎性假瘤组(P<0.001);游程长不均匀度(run length non-uniformity,RLNU)的4个参数在两组间差异无统计学意义(P>0.05)。在两组间差异有统计学意义的参数中,以GLNU的参数建立的多变量logistic回归模型的诊断效能最高,灵敏度、特异度及曲线下面积(area under curve,AUC)分别为95.9%、72.0%、0.905;PCA提取出的2个主成分建立的多变量logistic回归模型的灵敏度、特异度及AUC分别为87.5%、88.0%、0.913。结论:基于CE-T 1W的RLM特征可有效鉴别眼眶淋巴瘤与炎性假瘤,其中以GLNU的诊断效能最高。
文摘Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the effi cient development and utilization of deep CBM.
