Semantic Localization-driven Partial Image Retrieval in CT Series

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

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