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Proceedings Paper

Robust anatomy detection from CT topograms
Author(s): Zhigang Peng; Yiqiang Zhan; Xiang Sean Zhou; Arun Krishnan
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Paper Abstract

We present an automatic method to quickly and accurately detect multiple anatomy region-of-interests (ROIs) from CT topogram images. Our method first detects a redundant and potentially erroneous set of local features. Their spatial configurations are captured by a set of local voting functions. Unlike all the existing methods where the idea was to try to "hit" the correct/best constellations of local features, we have taken an opposite approach. We try to peel away the bad features until a safe (i.e., conservatively small) number of features remain. It is deterministic in nature and guarantees a success even for extremely noisy cases. The advantages of the method are its robustness and computational efficiency. Our method also addresses the potential scenario in which outliers (i.e., false landmarks detections) forms plausible configurations. As long as such outliers are a minority, the method can successfully remove these outliers. The final ROI of the anatomy is computed from a best subset of the remaining local features. Experimental validation was carried out for multiple organs detection from a large collection of CT topogram images. Fast and highly robust performance was observed. In the testing data sets, the detection rate varies from 98.2% to 100% for different ROIs and the false detection rate is from 0.0% to 0.5% for different ROIs. The method is fast and accurate enough to be seamlessly integrated into a real-time work flow on the CT machine to improve efficiency, consistency, and repeatability.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603X (27 February 2009); doi: 10.1117/12.813689
Show Author Affiliations
Zhigang Peng, Siemens Medical Solutions USA, Inc. (United States)
Yiqiang Zhan, Siemens Medical Solutions USA, Inc. (United States)
Xiang Sean Zhou, Siemens Medical Solutions USA, Inc. (United States)
Arun Krishnan, Siemens Medical Solutions USA, Inc. (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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