Student : Adrian – Alin BarglazanPaper :”Wall position and thickness estimation from sequences of images” - Dias, J.M.B.;   Leitao, J.M.N.;  

WALL POSITION AND THICKNESS ESTIMATION FROM SEQUENCES OF IMAGES

ECHOCARDIOGRAPHY - IMPORTANCE Ventricular contours Volume of chambers Thickness of myocardium Ventricular mass 3D reconstruction/modeling through

cardiac cycle

ECHOCARDIOGRAPHY

Advantages : Noninvasive agent Low cost Portability Real-time processing Direct 3D acquisition

ECHOCARDIOGRAPHY – MAIN DEGRADATION MECHANISMS

Side lobes Blur Poor Contrast Artifacts Speckle noise

PREVIOUS WORK

“Semiautomatic border tracking of cine echocardiogram ventricular images” – D.Adam, H. Harauveni, S. Sideman – 1987 :

Non linear median filter(9X9) of whole images. Location-dependent contrast stretching Tracks the movement of predetermined points which are manually defined

on the 2 myocardial border “Detecting left ventricular endocardial and epicardial

boundaries by two-dimensional” – C. Chu, E. Delp Edge detector – 41x41 Gaussian filter folowed by a Laplacian operator

The noise effects(speckle effect in principal) make conventional techniques based on edge enhancement inappropriate – gradient threshold, Laplace

PREVIOUS WORK

“Automated extraction of serial myocardial borders from an M-mode echocardiograms” – M. Unser, G. Pelle, P. Brun, M. Eden – 1989 :

Used suitable matched filters “Automatic ventricular cavity boundary detection from

sequential ultrasound images using simulated annealing “ - D. Adam

Proposed a fully automatic boundary detection from sequential images using simulated annealing .

PROPOSED APROACH

Image characterization – given a tissue, image is considered pixel wise independent .

Heart morphology – for example if we scan from inside to outside the values of the pixel should have a rectangular shape.

Contour model – contour sequences are assumed 2 dimensional Markov processes. Each random variable has a spatial index and a temporal index

Bayesian formulation and MAP IMDP – iterative multigrid dynamic programming –

to solve the problem of optimization

PROBABILISTIC MODEL OF ENDOCARDIAL AND EPICARDIAL CONTOURS

Represent the contour

Polar coordinates Heart contour Reflectivity

PROBABILISTIC MODEL OF ENDOCARDIAL AND EPICARDIAL CONTOURS

Echo along a radial scan-line from the heart center towards lung tissue.

THE MAIN ALGORITHM

RESULTS

RESULTS