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.

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)