{"title":"基于纹理的3D脑成像","authors":"Sagar Saladi, Pujita Pinnamaneni, Joerg Meyer","doi":"10.1109/BIBE.2001.974422","DOIUrl":null,"url":null,"abstract":"Different modalities in biomedical imaging, like CT, MRI and PET scanners,, provide detailed cross-sectional views of the human anatomy. The imagery obtained from these scanning devices are typically large-scale data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes, making them impossible to be stored on a regular local hard drive. San Diego Supercomputer Center (SDSC) maintains a high-performance storage system (HPSS) where these large-scale data sets can be stored. Members of the National Partnership for Advanced Computational Infrastructure (NPACI) have implemented a Scalable Visualization Toolkit (Vistools), which is used to access the data sets stored on HPSS and also to develop different applications on top of the toolkit. 2D cross-sectional images are extracted from the data sets stored oft HPSS using Vistools, and these 2D cross-sections are then transformed into smaller hierarchical representations using a wavelet transformation. This makes it easier to transmit them over the network and allows for progressive image refinement. The transmitted 2D cross-sections are then transformed and reconstructed into a 3D volume. The 3D reconstruction has been implemented using texture-mapping functions of Java3D. Sub-volumes that represent a region of interest are transmitted and rendered at a higher resolution than the rest of the data set.","PeriodicalId":405124,"journal":{"name":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Texture-based 3D brain imaging\",\"authors\":\"Sagar Saladi, Pujita Pinnamaneni, Joerg Meyer\",\"doi\":\"10.1109/BIBE.2001.974422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Different modalities in biomedical imaging, like CT, MRI and PET scanners,, provide detailed cross-sectional views of the human anatomy. The imagery obtained from these scanning devices are typically large-scale data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes, making them impossible to be stored on a regular local hard drive. San Diego Supercomputer Center (SDSC) maintains a high-performance storage system (HPSS) where these large-scale data sets can be stored. Members of the National Partnership for Advanced Computational Infrastructure (NPACI) have implemented a Scalable Visualization Toolkit (Vistools), which is used to access the data sets stored on HPSS and also to develop different applications on top of the toolkit. 2D cross-sectional images are extracted from the data sets stored oft HPSS using Vistools, and these 2D cross-sections are then transformed into smaller hierarchical representations using a wavelet transformation. This makes it easier to transmit them over the network and allows for progressive image refinement. The transmitted 2D cross-sections are then transformed and reconstructed into a 3D volume. The 3D reconstruction has been implemented using texture-mapping functions of Java3D. Sub-volumes that represent a region of interest are transmitted and rendered at a higher resolution than the rest of the data set.\",\"PeriodicalId\":405124,\"journal\":{\"name\":\"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2001.974422\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2001.974422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Different modalities in biomedical imaging, like CT, MRI and PET scanners,, provide detailed cross-sectional views of the human anatomy. The imagery obtained from these scanning devices are typically large-scale data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes, making them impossible to be stored on a regular local hard drive. San Diego Supercomputer Center (SDSC) maintains a high-performance storage system (HPSS) where these large-scale data sets can be stored. Members of the National Partnership for Advanced Computational Infrastructure (NPACI) have implemented a Scalable Visualization Toolkit (Vistools), which is used to access the data sets stored on HPSS and also to develop different applications on top of the toolkit. 2D cross-sectional images are extracted from the data sets stored oft HPSS using Vistools, and these 2D cross-sections are then transformed into smaller hierarchical representations using a wavelet transformation. This makes it easier to transmit them over the network and allows for progressive image refinement. The transmitted 2D cross-sections are then transformed and reconstructed into a 3D volume. The 3D reconstruction has been implemented using texture-mapping functions of Java3D. Sub-volumes that represent a region of interest are transmitted and rendered at a higher resolution than the rest of the data set.