
Proceedings Paper
DTI data modeling for unlimited query supportFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
This paper describes Data Modeling for unstructured data of Diffusion Tensor Imaging (DTI). Data Modeling is an
essential first step for data preparation in any data management and data mining procedure. Conventional Entity-
Relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with unstructured
image data associated with complex systems like the human brain. We propose a methodological framework for more
objective E-R data modeling with unlimited query support by eliminating the structured content-dependent metadata
associated with the unstructured data. The proposed method is applied to DTI data and a minimum system is
implemented accordingly. Eventually supported with navigation, data fusion, and feature extraction modules, the
proposed system provides a content-based support environment (C-BASE). Such an environment facilitates an unlimited
query support with a reproducible and efficient database schema. Switching between different modalities of data, while
confining the feature extractors within the object(s) of interest, we supply anatomically specific query results. The price
of such a scheme is relatively large storage and in some cases high computational cost. The data modeling and its
mathematical framework, behind the scene of query executions and the user interface of the system are presented in this
paper.
Paper Details
Date Published: 13 March 2009
PDF: 10 pages
Proc. SPIE 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 726415 (13 March 2009); doi: 10.1117/12.813748
Published in SPIE Proceedings Vol. 7264:
Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Khan M. Siddiqui M.D.; Brent J. Liu, Editor(s)
PDF: 10 pages
Proc. SPIE 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 726415 (13 March 2009); doi: 10.1117/12.813748
Show Author Affiliations
Mohammad-Reza Siadat, Oakland Univ. (United States)
Henry Ford Health System (United States)
Rafat Hammad, Oakland Univ. (United States)
Anil Shetty, William Beaumont Hospital (United States)
Hamid Soltanian-Zadeh, Henry Ford Health System (United States)
Univ. of Tehran (Iran, Islamic Republic of)
Henry Ford Health System (United States)
Rafat Hammad, Oakland Univ. (United States)
Anil Shetty, William Beaumont Hospital (United States)
Hamid Soltanian-Zadeh, Henry Ford Health System (United States)
Univ. of Tehran (Iran, Islamic Republic of)
Ishwar K. Sethi, Oakland Univ. (United States)
Ameen Eetemadi, Henry Ford Health System (United States)
Kost V. Elisevich, Henry Ford Health System (United States)
Ameen Eetemadi, Henry Ford Health System (United States)
Kost V. Elisevich, Henry Ford Health System (United States)
Published in SPIE Proceedings Vol. 7264:
Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Khan M. Siddiqui M.D.; Brent J. Liu, Editor(s)
© SPIE. Terms of Use
