1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain bounda...1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].展开更多
Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation...Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.展开更多
AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix metho...AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.展开更多
Based on the combination of Racah＇s group-theoretical consideration with Slater＇s wavefunction, a 91 ×91 complete energy matrix is established in tetragonal ligand field D2d for Pr3＋ ion. Thus, the Stark energ...Based on the combination of Racah＇s group-theoretical consideration with Slater＇s wavefunction, a 91 ×91 complete energy matrix is established in tetragonal ligand field D2d for Pr3＋ ion. Thus, the Stark energy-levels of Pr3＋ ions doped separately in LiYF4 and LiBiF4 crystals are calculated, and our calculations imply that the complete energy matrix method can be used as an effective tool to calculate the energy-levels of the systems doped by rare earth ions. Besides, the influence of Pr3＋ on energy-level splitting is investigated, and the similarities and the differences between the two doped crystals are demonstrated in detail by comparing their several pairs of curves and crystal field strength quantities. We see that the energy splitting patterns are similar and the crystal field interaction of LiYF4：Pr3＋ is stronger than that of LiBiF4：Pr3＋.展开更多
The mechanical properties of materials greatly depend on the microstructure morphology. The quantitative characterization of material microstructures is essential for the performance prediction and hence the material ...The mechanical properties of materials greatly depend on the microstructure morphology. The quantitative characterization of material microstructures is essential for the performance prediction and hence the material design. At present,the quantitative characterization methods mainly rely on the microstructure characterization of shape, size, distribution,and volume fraction, which related to the mechanical properties. These traditional methods have been applied for several decades and the subjectivity of human factors induces unavoidable errors. In this paper, we try to bypass the traditional operations and identify the relationship between the microstructures and the material properties by the texture of image itself directly. The statistical approach is based on gray level Co-occurrence matrix（GLCM）, allowing an objective and repeatable study on material microstructures. We first present how to identify GLCM with the optimal parameters, and then apply the method on three systems with different microstructures. The results show that GLCM can reveal the interface information and microstructures complexity with less human impact. Naturally, there is a good correlation between GLCM and the mechanical properties.展开更多
The macula is an imperative part present in our human visual system which is most responsible for clear and colour vision. For the people suffering from diabetes, the various parts of the body including the retina of ...The macula is an imperative part present in our human visual system which is most responsible for clear and colour vision. For the people suffering from diabetes, the various parts of the body including the retina of the eye are affected. These retinal damages cause swelling and other abnormalities nearby macula. The pathologies in macula due to diabetes are called Diabetic Macular oEdema (DME). It affects patients’ vision that may lead to vision loss. It can be overcome by advance identification of causes for swelling. The major causes for the swelling are neovascularization and other abnormalities occurring in the blood vessels nearby the macula. The aim of this work is to avoid vision loss by detecting the presence of abnormalities in macula in advance. The pathologies present in the abnormal images are detected by image segmentation technique viz. Fuzzy K-means algorithm. The classification is done by two different classifiers namely Cascade Neural Network and Partial Least Square which are employed to identify whether the image is normal or abnormal. The results of both the classifiers are compared with respect to classifier accuracy, sensitivity and specificity. The classifier accuracies of Cascade Neural Network and Partial Least Square are 96.84% and 94.36%, respectively. The information about the severity of the disease and the localization of pathologies are very useful to the ophthalmologist for diagnosing the disease and apply proper treatments to the patients to avoid the formation of any lesion and prevent vision loss.展开更多
针对传统卷积神经网络对微小热斑区域特征表达能力不足进而导致整体识别准确率下降的问题,提出了一种基于拓扑特征与纹理特征的图像重构(Image reconstruction based on topological and textural features,IR-TTF)算法。首先,将灰度化...针对传统卷积神经网络对微小热斑区域特征表达能力不足进而导致整体识别准确率下降的问题,提出了一种基于拓扑特征与纹理特征的图像重构(Image reconstruction based on topological and textural features,IR-TTF)算法。首先,将灰度化光伏红外热斑图像划分为多个互不重叠子区域。针对每一个子区域,利用持久同调提取增强的持久性条形码信息作为拓扑特征;同时利用灰度共生矩阵计算并放大对比度、同质性及能量特征作为纹理特征。最终,融合多个子区域的拓扑、纹理及灰度特征完成基于拓扑特征与纹理特征图像重构,进而构建新数据集,实现微小特征的增强表达。为验证IR-TTF算法有效性,利用3种对比卷积神经网络模型对重构数据集进行分类识别对比实验。实验表明,融合拓扑、纹理及灰度特征的数据集在3种卷积神经网络的平均识别准确率达到98%,较原数据集提高2.40%,较仅融合拓扑、灰度特征的数据集提高1.33%,从而验证了IR-TTF算法的有效性。展开更多
目的引入灰度共生矩阵,构建重症监护(intensive care unit,ICU)经口气管插管患者3个月非计划再住院的风险模型,为减少非计划再住院提供依据。方法采用便利抽样法,选取2023年11月—2024年5月河南省郑州市某三级甲等医院呼吸重症监护病房...目的引入灰度共生矩阵,构建重症监护(intensive care unit,ICU)经口气管插管患者3个月非计划再住院的风险模型,为减少非计划再住院提供依据。方法采用便利抽样法,选取2023年11月—2024年5月河南省郑州市某三级甲等医院呼吸重症监护病房260例经口气管插管患者作为研究对象。根据3个月内是否非计划再住院将其分为再住院组和未再住院组。收集患者的一般临床资料、实验室指标和拔管后第1天、第7天股直肌超声指标(灰度共生矩阵),采用多因素Logistic回归分析确定经口气管插管患者3个月非计划再住院的危险因素,采用Logistic回归和Framingham危险评分函数构建风险模型。采用受试者操作特征曲线(receiver operating characteristic curve,ROC)下面积(area under ROC curve,AUC)和Hosmer-Lemeshow评价模型的准确性。结果最终纳入224例。经口气管插管ICU患者3个月非计划再住院的发生率为35.71%(80/224)。年龄≥60岁、营养风险筛查≥3分、休克指数≥1.0、机械通气时长≥251h、拔管后第7天股直肌横截面积≤1.41cm2、拔管后第7天角二阶距≤0.71和拔管后第7天股直肌占股四头股的比率变化率为0是经口气管插管患者3个月再住院的危险因素(均P<0.05)。ROC曲线分析显示,气管插管患者3个月非计划再住院风险评分模型的AUC为0.791(95%CI:0.707~0.875,P<0.001),敏感度为75.02%,特异度为67.33%。Hosmer-Lemeshow结果显示χ2=2.581(P=0.630)。最佳临界值为3分。结论该风险评分模型具有良好的预测效能,可为临床医护人员评估经口气管插管患者的3个月非计划再住院的风险提供参考。展开更多
基金support by the National Natural Science Foundation of China(Grant Nos.U23A20546 and 52271010)the Chinese National Natural Science Fund for Distinguished Young Scholars(Grant No.52025015)the Natural Science Foundation of Tianjin City(No.21JCZDJC00510).
文摘1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].
文摘Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.
基金Supported by the Priming Scientific Research Foundation for the Junior Researcher in Beijing Tongren Hospital,Capital Medical University
文摘AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.
基金Foundation： Special program for public health of traditional Chinese medicine （Fiscal agency [ 2011 ] No.76） Special program for Chinese pharmaceutical industry （No.201207002）
基金Project supported by the National Natural Science Foundation of China(Grant Nos.10774103 and 10974138)
文摘Based on the combination of Racah＇s group-theoretical consideration with Slater＇s wavefunction, a 91 ×91 complete energy matrix is established in tetragonal ligand field D2d for Pr3＋ ion. Thus, the Stark energy-levels of Pr3＋ ions doped separately in LiYF4 and LiBiF4 crystals are calculated, and our calculations imply that the complete energy matrix method can be used as an effective tool to calculate the energy-levels of the systems doped by rare earth ions. Besides, the influence of Pr3＋ on energy-level splitting is investigated, and the similarities and the differences between the two doped crystals are demonstrated in detail by comparing their several pairs of curves and crystal field strength quantities. We see that the energy splitting patterns are similar and the crystal field interaction of LiYF4：Pr3＋ is stronger than that of LiBiF4：Pr3＋.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.5147113 and 51505037)the Fundamental Research Funds for the Central Universities of Ministry of Education of China(Grant Nos.3102017zy029,310832163402,and 310832163403)
文摘The mechanical properties of materials greatly depend on the microstructure morphology. The quantitative characterization of material microstructures is essential for the performance prediction and hence the material design. At present,the quantitative characterization methods mainly rely on the microstructure characterization of shape, size, distribution,and volume fraction, which related to the mechanical properties. These traditional methods have been applied for several decades and the subjectivity of human factors induces unavoidable errors. In this paper, we try to bypass the traditional operations and identify the relationship between the microstructures and the material properties by the texture of image itself directly. The statistical approach is based on gray level Co-occurrence matrix（GLCM）, allowing an objective and repeatable study on material microstructures. We first present how to identify GLCM with the optimal parameters, and then apply the method on three systems with different microstructures. The results show that GLCM can reveal the interface information and microstructures complexity with less human impact. Naturally, there is a good correlation between GLCM and the mechanical properties.
文摘The macula is an imperative part present in our human visual system which is most responsible for clear and colour vision. For the people suffering from diabetes, the various parts of the body including the retina of the eye are affected. These retinal damages cause swelling and other abnormalities nearby macula. The pathologies in macula due to diabetes are called Diabetic Macular oEdema (DME). It affects patients’ vision that may lead to vision loss. It can be overcome by advance identification of causes for swelling. The major causes for the swelling are neovascularization and other abnormalities occurring in the blood vessels nearby the macula. The aim of this work is to avoid vision loss by detecting the presence of abnormalities in macula in advance. The pathologies present in the abnormal images are detected by image segmentation technique viz. Fuzzy K-means algorithm. The classification is done by two different classifiers namely Cascade Neural Network and Partial Least Square which are employed to identify whether the image is normal or abnormal. The results of both the classifiers are compared with respect to classifier accuracy, sensitivity and specificity. The classifier accuracies of Cascade Neural Network and Partial Least Square are 96.84% and 94.36%, respectively. The information about the severity of the disease and the localization of pathologies are very useful to the ophthalmologist for diagnosing the disease and apply proper treatments to the patients to avoid the formation of any lesion and prevent vision loss.
文摘针对传统卷积神经网络对微小热斑区域特征表达能力不足进而导致整体识别准确率下降的问题,提出了一种基于拓扑特征与纹理特征的图像重构(Image reconstruction based on topological and textural features,IR-TTF)算法。首先,将灰度化光伏红外热斑图像划分为多个互不重叠子区域。针对每一个子区域,利用持久同调提取增强的持久性条形码信息作为拓扑特征;同时利用灰度共生矩阵计算并放大对比度、同质性及能量特征作为纹理特征。最终,融合多个子区域的拓扑、纹理及灰度特征完成基于拓扑特征与纹理特征图像重构,进而构建新数据集,实现微小特征的增强表达。为验证IR-TTF算法有效性,利用3种对比卷积神经网络模型对重构数据集进行分类识别对比实验。实验表明,融合拓扑、纹理及灰度特征的数据集在3种卷积神经网络的平均识别准确率达到98%,较原数据集提高2.40%,较仅融合拓扑、灰度特征的数据集提高1.33%,从而验证了IR-TTF算法的有效性。
文摘目的引入灰度共生矩阵,构建重症监护(intensive care unit,ICU)经口气管插管患者3个月非计划再住院的风险模型,为减少非计划再住院提供依据。方法采用便利抽样法,选取2023年11月—2024年5月河南省郑州市某三级甲等医院呼吸重症监护病房260例经口气管插管患者作为研究对象。根据3个月内是否非计划再住院将其分为再住院组和未再住院组。收集患者的一般临床资料、实验室指标和拔管后第1天、第7天股直肌超声指标(灰度共生矩阵),采用多因素Logistic回归分析确定经口气管插管患者3个月非计划再住院的危险因素,采用Logistic回归和Framingham危险评分函数构建风险模型。采用受试者操作特征曲线(receiver operating characteristic curve,ROC)下面积(area under ROC curve,AUC)和Hosmer-Lemeshow评价模型的准确性。结果最终纳入224例。经口气管插管ICU患者3个月非计划再住院的发生率为35.71%(80/224)。年龄≥60岁、营养风险筛查≥3分、休克指数≥1.0、机械通气时长≥251h、拔管后第7天股直肌横截面积≤1.41cm2、拔管后第7天角二阶距≤0.71和拔管后第7天股直肌占股四头股的比率变化率为0是经口气管插管患者3个月再住院的危险因素(均P<0.05)。ROC曲线分析显示,气管插管患者3个月非计划再住院风险评分模型的AUC为0.791(95%CI:0.707~0.875,P<0.001),敏感度为75.02%,特异度为67.33%。Hosmer-Lemeshow结果显示χ2=2.581(P=0.630)。最佳临界值为3分。结论该风险评分模型具有良好的预测效能,可为临床医护人员评估经口气管插管患者的3个月非计划再住院的风险提供参考。
