Pub Date : 2001-03-04DOI: 10.1109/BIBE.2001.974432
L. Ding, A. Goshtasby
A template-matching approach to registration of volumetric images is described. The process automatically selects about a dozen highly detailed and unique templates (cubic or spherical subvolumes) from the target volume and locates the templates in the reference volume. The centroids of four correspondences best satisfying the rigidity constraint are then used to determine the transformation matrix that resamples the target volume to overlay the reference volume. Different similarity measures used in template matching are discussed and experimental results are presented. The proposed registration method produces a median error of 2.8 mm when registering Venderbilt brain image data sets and an average registration time of 2.5 minutes on a 400 MHz PC.
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Pub Date : 2001-03-04DOI: 10.1109/BIBE.2001.974412
P. Lord, J. Reich, A. Mitchell, R. Stevens, T. Attwood, C. Goble
There have been several attempts at addressing the problem of annotating sequence data computationally. Annotation generation can be considered a pipeline of processes: first harvesting data from a variety of data sources, then distilling and transforming it into a form more appropriate for the end database. This task is usually performed by human annotators, a solution that is clearly not scaleable. There have been several attempts to mimic some of these pipelines in software. However, these have generally focused on low level annotation, such as database cross-references, or by harvesting data from computational techniques such as gene finding or similarity searches. Higher level annotation such as that seen in the PRINTS database is usually formed from data that is free text, or only partly structured. This presents a much greater computational challenge. Therefore we studied the pipeline that is used to generate annotation for the PRINTS database, and have developed prototype software that reflects and automates this pipeline. As this software operates primarily on data culled from the SWISS-PROT database, we have called it PRECIS (Protein Reports Engineered from Concise Information in SWISS-PROT). This software is currently being used to generate annotation for the prePRINTS database. As the output is a structured report detailing the function, structure and disease associations of a protein, and providing literature references and keywords we believe it will be of more generic use. The software is available on request from mitchell@bioinf.man.ac.uk.
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Pub Date : 2001-03-04DOI: 10.1109/BIBE.2001.974405
H. Jamil, Giovanni A. Modica, Maria A. Teran
Querying and visualization of phylogenetic databases remain a great challenge due to their inherent complex structures. Popular phylogenetic databases such as Tree of Life and TreeBASE do not support flexible querying through query languages for the exploration of their contents. The query facility employed in these databases is usually limited to complex interfaces or is too limited to be useful for many applications. The most striking shortcoming of these systems is that they do not treat phylogenies (trees) as first citizens. In this paper, we introduce a novel visual query language for phylogenetic databases in which trees are recognized as basic units. We also introduce a Web based query interface, based on this language, for querying any tree like structure, either on the Web (e.g. Tree of Life), or in traditional relational databases (e.g. TreeBASE). As an aside, the mapping technique used in our system makes it possible to interoperate between a variety of heterogeneous phylogenetic databases. Finally, we demonstrate that the basic tree manipulation operators proposed in this paper can be used to form unlimited types of tree queries that were not possible in popular phylogenetic databases until now.
系统发育数据库由于其固有的复杂结构，查询和可视化仍然是一个巨大的挑战。流行的系统发育数据库，如Tree of Life和TreeBASE，不支持通过查询语言对其内容进行灵活的查询。这些数据库中使用的查询功能通常仅限于复杂的接口，或者对许多应用程序都不太有用。这些系统最显著的缺点是它们没有把系统发育(树)当作第一公民来对待。本文介绍了一种以树为基本单位的系统发育数据库可视化查询语言。我们还介绍了基于这种语言的基于Web的查询接口，用于在Web(例如tree of Life)或传统关系数据库(例如TreeBASE)中查询任何树状结构。顺便说一句，在我们的系统中使用的映射技术使得在各种异构的系统发育数据库之间进行互操作成为可能。最后，我们证明了本文提出的基本树操作算子可以用来形成无限类型的树查询，这在目前流行的系统发育数据库中是不可能的。
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Pub Date : 1900-01-01DOI: 10.1109/BIBE.2001.974422
Sagar Saladi, Pujita Pinnamaneni, Joerg Meyer
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.
{"title":"Texture-based 3D brain imaging","authors":"Sagar Saladi, Pujita Pinnamaneni, Joerg Meyer","doi":"10.1109/BIBE.2001.974422","DOIUrl":"https://doi.org/10.1109/BIBE.2001.974422","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":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121275174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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