Semantic localization-driven partial image retrieval in CT series

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.

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.

We employ interpolation and regression techniques for mapping the slices of a series to a newly generated standardized height atlas of the human body.

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.

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.