2026-09-19T18:09:45Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416052022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Polina Golland.
Kang, InHan
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T15:59:50Z
2008-05-19T15:59:50Z
2006
2006
http://hdl.handle.net/1721.1/41605
214339717
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 65-66).
In this thesis, we present a system for visualizing hierarchical, multi-dimensional, memory-intensive datasets. Specifically, we designed an interactive system to visualize data collected by high-throughput microscopy and processed by CellProfiler, an open-source system jointly developed by researchers at MIT CSAIL and the White-head Institute. A typical high-throughput microscopy experiment produces thousands of images, with thousands of objects in each image. CellProfiler then measures hundreds of features for each cell, nuclei, and cytoplasm. In contrast to previously demonstrated visualization software, our system visualizes datasets that are on the order of hundreds of gigabytes, datasets too large to store in physical memory. We also implement tools to link the dataset to available resources such as online genetic databases and the actual images acquired by the microscope. Finally, we demonstrate how the system was used to highlight interesting genes for more detailed analysis in real biological studies.
by InHan Kang.
M.Eng.
66 p.
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.
CellVisualizer : exploring hierarchical, multi-dimensional data with applications to high-throughput microscopy
Cell Visualizer : exploring hierarchical, multi-dimensional data with applications to high-throughput microscopy
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0a9a242c-4665-4f77-be89-2523ff3757ed">
<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>CellVisualizer : exploring hierarchical, multi-dimensional data with applications to high-throughput microscopy</Title>
<Subtitle>Cell Visualizer : exploring hierarchical, multi-dimensional data with applications to high-throughput microscopy</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2006</PublicationDate>
<Authors>
<Author>
<DisplayName>Kang, InHan</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>In this thesis, we present a system for visualizing hierarchical, multi-dimensional, memory-intensive datasets. Specifically, we designed an interactive system to visualize data collected by high-throughput microscopy and processed by CellProfiler, an open-source system jointly developed by researchers at MIT CSAIL and the White-head Institute. A typical high-throughput microscopy experiment produces thousands of images, with thousands of objects in each image. CellProfiler then measures hundreds of features for each cell, nuclei, and cytoplasm. In contrast to previously demonstrated visualization software, our system visualizes datasets that are on the order of hundreds of gigabytes, datasets too large to store in physical memory. We also implement tools to link the dataset to available resources such as online genetic databases and the actual images acquired by the microscope. Finally, we demonstrate how the system was used to highlight interesting genes for more detailed analysis in real biological studies.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>