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Sebastian Seung. Shearer, Rachel Welles Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-03-25T15:06:07Z 2010-03-25T15:06:07Z 2009 2009 http://hdl.handle.net/1721.1/53139 505508628 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Includes bibliographical references (leaf 69). Connectomics researchers examine images of the brain in order to determine the structure of neuronal networks. As imaging techniques improve, images are growing in size and resolution - but they are also outgrowing the capacity of existing software to view these images. In response to this problem, this thesis presents OMNI: an application for viewing and editing large connectomic image volumes. OMNI employs pre-processing and caching techniques to allow researchers to examine large image volumes at multiple viewpoints and resolutions. But OMNI is also a full-fledged navigation and editing environment, incorporating the suggestions of connectomics researchers into a simple and flexible user interface design. The OMNI user interface features multiple synchronized display windows and a novel project inspector widget that facilitates project interaction. The 2D navigation and editing modules use OpenGL textures to display image slices from large image volumes and feature a texture management system that includes a threaded texture cache. Editing is performed by painting voxels in a viewing window and allows the user to edit existing neuron tracings or create new ones. The development of OMNI gives connectomics researchers a way to view detailed images of the nervous system and enables them to trace neural pathways through these large images. By studying the structure of individual neurons and groups of neurons, researchers can approach a better understanding of neuron function and the development of the brain. by Rachel Welles Shearer. M.Eng. 69 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Omni : visualizing and editing large-scale volume segmentations of neuronal tissue Visualizing and editing large-scale volume segmentations of neuronal tissue Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="aed69414-8607-4c2a-bb18-d1df5f6f6943"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Omni : visualizing and editing large-scale volume segmentations of neuronal tissue</Title> <Subtitle>Visualizing and editing large-scale volume segmentations of neuronal tissue</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Shearer, Rachel Welles</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Connectomics researchers examine images of the brain in order to determine the structure of neuronal networks. As imaging techniques improve, images are growing in size and resolution - but they are also outgrowing the capacity of existing software to view these images. In response to this problem, this thesis presents OMNI: an application for viewing and editing large connectomic image volumes. OMNI employs pre-processing and caching techniques to allow researchers to examine large image volumes at multiple viewpoints and resolutions. But OMNI is also a full-fledged navigation and editing environment, incorporating the suggestions of connectomics researchers into a simple and flexible user interface design. The OMNI user interface features multiple synchronized display windows and a novel project inspector widget that facilitates project interaction. The 2D navigation and editing modules use OpenGL textures to display image slices from large image volumes and feature a texture management system that includes a threaded texture cache. Editing is performed by painting voxels in a viewing window and allows the user to edit existing neuron tracings or create new ones. The development of OMNI gives connectomics researchers a way to view detailed images of the nervous system and enables them to trace neural pathways through these large images. By studying the structure of individual neurons and groups of neurons, researchers can approach a better understanding of neuron function and the development of the brain.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>