Summary
Background: Picture archiving and communication systems (PACS) contain very large amounts of
computed tomography (CT) data. When querying a PACS for a particular series, the user
is often not interested in the complete series but in a certain region of interest
(ROI), described e.g. by an example view in another series or an anatomical concept.
Objectives: Restricting a retrieval query to such an ROI saves both loading time and navigational
effort. In this paper, we propose an efficient method for defining and retrieving
ROIs.
Methods: We employ interpolation and regression techniques for mapping the slices of a series
to a newly generated standardized height atlas of the human body.
Results: Examinations of the accuracy and the saved input/output (I/O) costs of our new method
on a repository of 1,360 CT series demonstrate the advantages of our system. Depending
on the scope of the retrieval query, we can economize up to 99% of the total loading
time.
Conclusion: Our proposed method for flexible, context-based, partial image retrieval enables
the user to directly focus on the relevant portion of the image material and it targets
the high potential of I/O cost reduction of a common PACS.
Keywords
Information storage and retrieval - medical imaging - body regions - semantics - context-based
image retrieval
