Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im...Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.展开更多
Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for au...Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.展开更多
A wide range of techniques has been developed to image biological samples at high spatial and temporal resolution.In this paper,we report recent results from deep-UV confocal fAuorescence microscopy to image inherent ...A wide range of techniques has been developed to image biological samples at high spatial and temporal resolution.In this paper,we report recent results from deep-UV confocal fAuorescence microscopy to image inherent emission from fuorophores such as tryptophan,and structured ilumination microscopy(SIM)of biological materials.One motivation for developing deep-UV fhuorescence imaging and SIM is to provide methods to complement our measurements in the emerging field of X-ray coherent diffractive imaging.展开更多
To attain the volumetric information of the optic radiation in normal human brains, we per- formed diffusion tensor imaging examination in 13 healthy volunteers. Simultaneously, we used a brain normalization method to...To attain the volumetric information of the optic radiation in normal human brains, we per- formed diffusion tensor imaging examination in 13 healthy volunteers. Simultaneously, we used a brain normalization method to reduce individual brain variation and increase the accuracy of volumetric information analysis. In addition, tractography-based group mapping method was also used to investigate the probability and distribution of the optic radiation pathways. Our results showed that the measured optic radiation fiber tract volume was a range of about 0.16% and that the fractional anisotropy value was about 0.53. Moreover, the optic radiation probability fiber pathway that was determined with diffusion tensor tractography-based group mapping was able to detect the location relatively accurately. We believe that our methods and results are help- ful in the study of optic radiation fiber tract information.展开更多
AIM:To evaluate the diagnosis of different differentiated gastric intraepithelial neoplasia (IN) by magnifica-tion endoscopy combined with narrow-band imaging (ME-NBI) and confocal laser endomicroscopy (CLE). METHODS:...AIM:To evaluate the diagnosis of different differentiated gastric intraepithelial neoplasia (IN) by magnifica-tion endoscopy combined with narrow-band imaging (ME-NBI) and confocal laser endomicroscopy (CLE). METHODS:Eligible patients with suspected gastric IN lesions previously diagnosed by endoscopy in secondary hospitals and scheduled for further diagnosis and tratment were recruited for this study. Excluded from the study were patients who had liver cirrhosis, impaired renal function, acute gastrointestinal (GI) bleeding, coagulopathy, esophageal varices, jaundice, and GI post-surgery. Also excluded were those who were pregnant, breastfeeding, were younger than 18 years old, or were unable to provide informed consent. All patients had all mucus and bile cleared from their stom-achs. They then received upper GI endoscopy. When a mucosal lesion is found during observation with whitelight imaging, the lesion is visualized using maximal magnification, employing gradual movement of the tip of the endoscope to bring the image into focus. Saved images are analyzed. Confocal images were evaluated by two endoscopists (Huang J and Li MY), who were familiar with CLE, blinded to the related information about the lesions, and asked to classify each lesion as either a low grade dysplasia (LGD) or high grade dysplasia (HGD) according to given criteria. The results were compared with the final histopathologic diagnosis. ME-NBI images were evaluated by two endoscopists (Lu ZS and Ling-Hu EQ) who were familiar with NBI, blinded to the related information about the lesions and CLE images, and were asked to classify each lesion as a LGD or HGD according to the "microvascular pattern and surface pattern" classification system. The results were compared with the final histopathologic diagnosis. RESULTS: The study included 32 pathology-proven low grade gastric IN and 26 pathology-proven high grade gastric IN that were detected with any of the modalities. CLE and ME-NBI enabled clear visualization of the vascular microsurface patterns and microvascular structures of the gastric mucosa. The accuracy of the CLE and the ME-NBI diagnosis was 88% (95% CI:78%-98%) and 81% (95% CI: 69%-93%), respectively. The kappa coefficient of agreement between the histopathology and the in vivo CLE imaging was 0.755; between the histopathology and the in vivo CLE imaging was 0.615. McNemar's test (binomial distribution used) indicated that the agreement was significant (P < 0.05). When patients were diagnosed by MENBI with CLE, the overall accuracy of the diagnosis was 86.21% (95% CI:73%-96%), and the kappa coefficient of agreement was 0.713, according to McNemar's test (P < 0.05). CONCLUSION:Higher diagnostic accuracy, sensitivityand specificity of CLE over ME-NBI indicate the feasibility of these two techniques for the efficacious diagnostic classification of gastric IN.展开更多
It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon O...It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.展开更多
Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usual...Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.展开更多
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
基金Supported by the National Basic Research Priorities Programme(No.2007CB311004)the National Natural Science Foundation of China(No.61035003,60933004,60903141,60970088,61072085)
文摘Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.
基金We acknowledge the support of the Australian Research Council for the Center of Excellence for Coherent X-ray Science(CE0561787).
文摘A wide range of techniques has been developed to image biological samples at high spatial and temporal resolution.In this paper,we report recent results from deep-UV confocal fAuorescence microscopy to image inherent emission from fuorophores such as tryptophan,and structured ilumination microscopy(SIM)of biological materials.One motivation for developing deep-UV fhuorescence imaging and SIM is to provide methods to complement our measurements in the emerging field of X-ray coherent diffractive imaging.
文摘To attain the volumetric information of the optic radiation in normal human brains, we per- formed diffusion tensor imaging examination in 13 healthy volunteers. Simultaneously, we used a brain normalization method to reduce individual brain variation and increase the accuracy of volumetric information analysis. In addition, tractography-based group mapping method was also used to investigate the probability and distribution of the optic radiation pathways. Our results showed that the measured optic radiation fiber tract volume was a range of about 0.16% and that the fractional anisotropy value was about 0.53. Moreover, the optic radiation probability fiber pathway that was determined with diffusion tensor tractography-based group mapping was able to detect the location relatively accurately. We believe that our methods and results are help- ful in the study of optic radiation fiber tract information.
文摘AIM:To evaluate the diagnosis of different differentiated gastric intraepithelial neoplasia (IN) by magnifica-tion endoscopy combined with narrow-band imaging (ME-NBI) and confocal laser endomicroscopy (CLE). METHODS:Eligible patients with suspected gastric IN lesions previously diagnosed by endoscopy in secondary hospitals and scheduled for further diagnosis and tratment were recruited for this study. Excluded from the study were patients who had liver cirrhosis, impaired renal function, acute gastrointestinal (GI) bleeding, coagulopathy, esophageal varices, jaundice, and GI post-surgery. Also excluded were those who were pregnant, breastfeeding, were younger than 18 years old, or were unable to provide informed consent. All patients had all mucus and bile cleared from their stom-achs. They then received upper GI endoscopy. When a mucosal lesion is found during observation with whitelight imaging, the lesion is visualized using maximal magnification, employing gradual movement of the tip of the endoscope to bring the image into focus. Saved images are analyzed. Confocal images were evaluated by two endoscopists (Huang J and Li MY), who were familiar with CLE, blinded to the related information about the lesions, and asked to classify each lesion as either a low grade dysplasia (LGD) or high grade dysplasia (HGD) according to given criteria. The results were compared with the final histopathologic diagnosis. ME-NBI images were evaluated by two endoscopists (Lu ZS and Ling-Hu EQ) who were familiar with NBI, blinded to the related information about the lesions and CLE images, and were asked to classify each lesion as a LGD or HGD according to the "microvascular pattern and surface pattern" classification system. The results were compared with the final histopathologic diagnosis. RESULTS: The study included 32 pathology-proven low grade gastric IN and 26 pathology-proven high grade gastric IN that were detected with any of the modalities. CLE and ME-NBI enabled clear visualization of the vascular microsurface patterns and microvascular structures of the gastric mucosa. The accuracy of the CLE and the ME-NBI diagnosis was 88% (95% CI:78%-98%) and 81% (95% CI: 69%-93%), respectively. The kappa coefficient of agreement between the histopathology and the in vivo CLE imaging was 0.755; between the histopathology and the in vivo CLE imaging was 0.615. McNemar's test (binomial distribution used) indicated that the agreement was significant (P < 0.05). When patients were diagnosed by MENBI with CLE, the overall accuracy of the diagnosis was 86.21% (95% CI:73%-96%), and the kappa coefficient of agreement was 0.713, according to McNemar's test (P < 0.05). CONCLUSION:Higher diagnostic accuracy, sensitivityand specificity of CLE over ME-NBI indicate the feasibility of these two techniques for the efficacious diagnostic classification of gastric IN.
基金support from the Strategic Pilot Science and Technology project of the Chinese Academy of Sciences(Grant No.XDA05040200)the National Natural Science Foundation of China(Grant No.41275040)
文摘It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.
文摘Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.
