Scale Invariant Feature Transform as feature tracking method in 4D imaging: a feasibility study.

C. Paganelli, M. Peroni, F. Pennati, G. Baroni, P. Summers, M. Bellomi, M. Riboldi

Research output: Contribution to journalArticle

Abstract

We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as an operator-independent feature tracking method.

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Feasibility Studies
Mathematical transformations
Imaging techniques
Four-Dimensional Computed Tomography
Liver
Magnetic resonance imaging
Mathematical operators
Lung

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Scale Invariant Feature Transform as feature tracking method in 4D imaging: a feasibility study.",
abstract = "We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as an operator-independent feature tracking method.",
author = "C. Paganelli and M. Peroni and F. Pennati and G. Baroni and P. Summers and M. Bellomi and M. Riboldi",
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AU - Pennati, F.

AU - Baroni, G.

AU - Summers, P.

AU - Bellomi, M.

AU - Riboldi, M.

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AB - We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as an operator-independent feature tracking method.

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