Wall position and thickness estimation from sequences of images

1. Student : Adrian – Alin Barglazan Paper :”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

2. Ventricular contours Volume of chambers Thickness of myocardium Ventricular mass 3D reconstruction/modeling through cardiac cycle Echocardiography - IMPORTANCE

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

4. Echocardiography – main degradation mechanisms • Side lobes • Blur • Poor Contrast • Artifacts • Speckle noise

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

6. “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 . Previous WORk

7. 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 Proposed aproach

8. Represent the contour Polar coordinates Heart contour Reflectivity Probabilistic model of endocardial and epicardial contours

9. Echo along a radial scan-line from the heart center towards lung tissue. Probabilistic model of endocardial and epicardial contours

10. THE main algorithm

11. RESULTS

12. RESULTS