Paper
26 March 2008 Segmentation of myocardial perfusion MR sequences with multi-band active appearance models driven by spatial and temporal features
N. Baka, J. Milles, E. A. Hendriks, A. Suinesiaputra, M. Jerosch Herold, J. H. C. Reiber, B. P. F. Lelieveldt
Author Affiliations +
Abstract
This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a strict need for temporal correspondence across the image sequences. We also investigate which combination of spatial and temporal features yields the best segmentation performance. Our evaluation criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity projection feature and gradient orientation map yields the best segmentation performance, with an average point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal features does not necessarily increase performance.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
N. Baka, J. Milles, E. A. Hendriks, A. Suinesiaputra, M. Jerosch Herold, J. H. C. Reiber, and B. P. F. Lelieveldt "Segmentation of myocardial perfusion MR sequences with multi-band active appearance models driven by spatial and temporal features", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691415 (26 March 2008); https://doi.org/10.1117/12.772660
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Cited by 7 scholarly publications.
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KEYWORDS
Image segmentation

RGB color model

Statistical modeling

Error analysis

Heart

Magnetic resonance imaging

Principal component analysis

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