Wall position and thickness estimation from sequences of echocardiographic images

Abstract
This paper presents a new method for endocardial (inner) and epicardial (outer) con - tour estimation from sequences of echocardiographic images The framework herein introduced is ne - tuned for parasternal short axis views at the papillary muscle level The underlying model is probabilistic; it captures the relevant features of the image generation physical mechanisms and of the heart morphology Contour sequences are assumed two - dimensional noncausal rst order Markov random processes; each variable has a spatial index and a temporal index The image pixels are modelled as Rayleigh distributed random variables with means depending on their positions (in - side endocardium, between endocardium and pericardium, or outside pericardium) The complete probabilistic model is built under the Bayesian framework As estima - tion criterion the maximum a posteriori (MAP) is adopted To solve the optimization problem one is led to (joint estimation of contours and distributions' parameters), we introduce an algorithm herein named iterative multigrid dynamic programming (IMDP) It is a fully data driven scheme with no ad - hoc parameters The method is imple - mented on an ordinary workstation, leading to computation times compatible with operational use Experiments with simulated and real images are presented

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