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Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform

Khoa Tan Nguyen
Scientific Visualization Group, Linköping University, Sweden

Timo Ropinski
Scientific Visualization Group, Linköping University, Sweden

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Ingår i: Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden

Linköping Electronic Conference Proceedings 94:2, s. 11-16

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Publicerad: 2013-11-04

ISBN: 978-91-7519-455-4

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

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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