A stochastic method for three-dimensional (3D) coronary motion compensation from a monoplane temporal sequence of X-Ray angiographic frames is presented. The 3D coronary centerline is segmented from a preoperative multi-sclice computed tomography (MSCT), and subject to a non-rigid deformation model with few parameters. The 2D angiographic frames are segmented as well using a multiscale vesselness filter, thresholded, and skeletonized to obtain the 2D binary centerline of the coronary artery tree at each time.
A generative model is then introduced to model the process that results in the observed angiographic frames as a stochastic process, namely a Hidden Markov model. This latter is used along with a particle filter to constrain the variations of the deformation model’s parameters over time, and relies on a feature-based cosine similarity measure involving a distance transformation on the binary coronary centerline images. Three-dimensional registration is performed in a projective and iterative manner.
Validation is carried out first through a set of simulations with real 3D coronary centerlines, and eventually with one 3D centerline and the associated angiographic sequence.
| Date | 17 Feb 2012 |
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| Original language | French |
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| Awarding Institution | - École de technologie supérieure
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| Supervisor | Luc Duong (Supervisor) & Christian Desrosiers (Co-supervisor) |
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Hadida, J. (Author),
Duong (Supervisor) &
Desrosiers (Co-supervisor),
17 Feb 2012Student thesis: Master's thesis › Master in Engineering: Engineering