Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques ar...Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.展开更多
With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Op...With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.展开更多
基金supported by “the Fundamental Research Funds for the Central Universities” No.2018CUCTJ081
文摘Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.
基金supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB38050300)the Genomics Data Center Operation and Maintenance of Chinese Academy of Sciences(Grant No.CAS-WX2022SDC-XK05)the Key Technology Talent Program of the Chinese Academy of Sciences,China.
文摘With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.
