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Methods Inf Med. 2012;51(6):557-65. doi: 10.3414/ME11-02-0028. Epub 2012 Nov 16.

Semantic localization-driven partial image retrieval in CT series.

Author information

1
Institute for Informatics, Ludwig-Maximilians-Universit√§t M√ľnchen, Oettingenstr. 67, 80538 Munich, Germany.

Abstract

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.

PMID:
23154618
DOI:
10.3414/ME11-02-0028
[Indexed for MEDLINE]
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