Shantanu H Joshi, Ian Bowman, John Darrell Van Horn
{"title":"Large-scale Neuroanatomical Visualization Using a Manifold Embedding Approach.","authors":"Shantanu H Joshi, Ian Bowman, John Darrell Van Horn","doi":"10.1109/VAST.2010.5652532","DOIUrl":"10.1109/VAST.2010.5652532","url":null,"abstract":"<p><p>We present a unified framework for data processing, mining and interactive visualization of large-scale neuroanatomical databases. The input data is assumed to lie in a specific atlas space, or simply exist as a separate collection. Users can specify their own atlas for comparative analyses. The original data exist as MRI images in standard formats. It is uploaded to a remote server and processed offline by a parallelized pipeline workflow. This workflow transforms the data to represent it as both volumetric and triangular mesh cortical surfaces. We use multiresolution representations to scale complexity to data storage availability as well as graphical processing performance. Our workflow implements predefined metrics for clustering and classification, and data projection schemes to aid in visualization. Additionally the system provides a visual query interface for performing selection requests based on user-defined search criteria.</p>","PeriodicalId":88970,"journal":{"name":"Proceedings. IEEE Symposium on Visual Analytics Science and Technology","volume":"2010 25-26 Oct. 2010","pages":"237"},"PeriodicalIF":0.0,"publicationDate":"2010-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3037590/pdf/nihms264009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29669290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scott Barlowe, Tianyi Zhang, Yujie Liu, Jing Yang, Donald Jacobs
{"title":"Multivariate Visual Explanation for High Dimensional Datasets.","authors":"Scott Barlowe, Tianyi Zhang, Yujie Liu, Jing Yang, Donald Jacobs","doi":"10.1109/VAST.2008.4677368","DOIUrl":"10.1109/VAST.2008.4677368","url":null,"abstract":"<p><p>Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach.</p>","PeriodicalId":88970,"journal":{"name":"Proceedings. IEEE Symposium on Visual Analytics Science and Technology","volume":"2008 ","pages":"147-154"},"PeriodicalIF":0.0,"publicationDate":"2008-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2916668/pdf/nihms90628.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29175482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}