The quality of ultrasound scanning images is usually damaged by speckle noise.This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing alg...The quality of ultrasound scanning images is usually damaged by speckle noise.This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm.Unlike single statistical moment-based speckle reduction algorithms,this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability.The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance.Then,according to the similarity value and tissue characteristics,the entire image is divided into several levels of speckle-content regions,and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique.Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.展开更多
Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology ...Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology （VO） were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results： A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables （e.g., microarray type and human variations）. Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions： The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.展开更多
基金This study is supported by the Chunhui Project(No.Z2015108)the Ministry of Education China,the Sichuan Science and Technology Program(No.2019YFG0196)+2 种基金the high-level personnel launch scientific research projects of Guizhou Institute of Technology(No.XJGC 20150105)the Science&Technology Department of Guizhou Province and Guizhou Institute of Technology Collaborative Fund LH(No.[2015]7104)the invitation for bid Project of Education Department of Guizhou Province KY(No.[2015]360).
文摘The quality of ultrasound scanning images is usually damaged by speckle noise.This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm.Unlike single statistical moment-based speckle reduction algorithms,this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability.The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance.Then,according to the similarity value and tissue characteristics,the entire image is divided into several levels of speckle-content regions,and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique.Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.
文摘Background： The community-based Ontology of Biological and Clinical Statistics （OBCS） represents and standardizes biological and clinical data and statistical methods. Methods： Both OBCS and the Vaccine Ontology （VO） were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results： A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables （e.g., microarray type and human variations）. Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions： The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.
