Khoa Tan Nguyen
Scientific Visualization Group, Linköping University, Sweden
Timo Ropinski
Scientific Visualization Group, Linköping University, Sweden
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Published in: Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden
Linköping Electronic Conference Proceedings 94:2, p. 11-16
Published: 2013-11-04
ISBN: 978-91-7519-455-4
ISSN: 1650-3686 (print), 1650-3740 (online)
Recent advances in medical imaging technology enable dynamic acquisitions of objects under movement. The acquired dynamic data has shown to be useful in different application scenarios. However; the vast amount of timevarying data put a great demand on robust and efficient algorithms for extracting and interpreting the underlying information. In this paper; we present a gpu-based approach for feature tracking in time-varying volumetric data set based on the Scale Invariant Feature Transform (SIFT) algorithm. Besides; the improved performance; this enables us to robustly and efficiently track features of interest in the volumetric data over the time domain. As a result; the proposed approach can serve as a foundation for more advanced analysis on the features of interest in dynamic data sets. We demonstrate our approach using a time-varying data set for the analysis of internal motion of breathing lungs.
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