NDLI: Database design and implementation for quantitative image analysis research

Content Provider	IEEE Xplore Digital Library
Author	Brown, M.S. Shah, S.K. Pais, R.C. Yeng-Zhong Lee McNitt-Gray, M.F. Goldin, J.G. Cardenas, A.F. Aberle, D.R.
Copyright Year	1997
Abstract	Quantitative image analysis (QIA) goes beyond subjective visual assessment to provide computer measurements of the image content, typically following image segmentation to identify anatomical regions of interest (ROIs). Commercially available picture archiving and communication systems focus on storage of image data. They are not well suited to efficient storage and mining of new types of quantitative data. In this paper, we present a system that integrates image segmentation, quantitation, and characterization with database and data mining facilities. The paper includes generic process and data models for QIA in medicine and describes their practical use. The data model is based upon the Digital Imaging and Communications in Medicine (DICOM) data hierarchy, which is augmented with tables to store segmentation results (ROIs) and quantitative data from multiple experiments. Data mining for statistical analysis of the quantitative data is described along with example queries. The database is implemented in PostgreSQL on a UNIX server. Database requirements and capabilities are illustrated through two quantitative imaging experiments related to lung cancer screening and assessment of emphysema lung disease. The system can manage the large amounts of quantitative data necessary for research, development, and deployment of computer-aided diagnosis tools.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	10
File Size	906839
Starting Page	99
Ending Page	108
File Format	PDF
ISSN	10897771
Volume Number	9
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image databases Image analysis Image segmentation Image storage Data mining Data models Lungs Visual databases Picture archiving and communication systems Biomedical imaging image analysis database systems
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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