Abstract
This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provide real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live motion-compensation in modern MR scanners.
Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape.
Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.
Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape.
Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.
Original language | English |
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Title of host publication | Functional Imaging and Modeling of the Heart, FIMH 2003 |
Publisher | Springer Verlag |
Publication date | 2003 |
Pages | 151-161 |
DOIs | |
Publication status | Published - 2003 |
Event | Functional Imaging and Modeling of the Heart, FIMH 2003 - Duration: 1 Jan 2003 → … |
Conference
Conference | Functional Imaging and Modeling of the Heart, FIMH 2003 |
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Period | 01/01/2003 → … |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 2674 |
ISSN | 0302-9743 |