A hierarchical feature based deformation model applied to 4D cardiac SPECT data
Publication
, Conference
Laading, JK; McCulloch, C; Johnson, VE; Gilland, DR; Jaszczak, RJ
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 1999
In this paper we describe a statistical model for the observation of labeled points in gated cardiac single photon emission computed tomography (SPECT) images. The model has two major parts: one based on shape correspondence between the image for evaluation and a reference image, and a second based on the match in image features. While the statistical deformation model is applicable to a broad range of image objects, the addition of a contraction mechanism to the baseline model provides particularly convincing results in gated cardiac SPECT. The model is applied to clinical data and provides marked improvement in the quality of summary images for the time series. Estimates of heart deformation and contraction parameters are also obtained.
Duke Scholars
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
ISBN
9783540661672
Publication Date
January 1, 1999
Volume
1613
Start / End Page
266 / 279
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
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Laading, J. K., McCulloch, C., Johnson, V. E., Gilland, D. R., & Jaszczak, R. J. (1999). A hierarchical feature based deformation model applied to 4D cardiac SPECT data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1613, pp. 266–279). https://doi.org/10.1007/3-540-48714-x_20
Laading, J. K., C. McCulloch, V. E. Johnson, D. R. Gilland, and R. J. Jaszczak. “A hierarchical feature based deformation model applied to 4D cardiac SPECT data.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1613:266–79, 1999. https://doi.org/10.1007/3-540-48714-x_20.
Laading JK, McCulloch C, Johnson VE, Gilland DR, Jaszczak RJ. A hierarchical feature based deformation model applied to 4D cardiac SPECT data. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1999. p. 266–79.
Laading, J. K., et al. “A hierarchical feature based deformation model applied to 4D cardiac SPECT data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1613, 1999, pp. 266–79. Scopus, doi:10.1007/3-540-48714-x_20.
Laading JK, McCulloch C, Johnson VE, Gilland DR, Jaszczak RJ. A hierarchical feature based deformation model applied to 4D cardiac SPECT data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1999. p. 266–279.
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
ISBN
9783540661672
Publication Date
January 1, 1999
Volume
1613
Start / End Page
266 / 279
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences