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

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Presentation on theme: "Student : Adrian – Alin Barglazan Paper :”Wall position and thickness estimation from sequences of images” - Dias, J.M.B.; Leitao, J.M.N.;"— Presentation transcript:

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

2  Ventricular contours  Volume of chambers  Thickness of myocardium  Ventricular mass  3D reconstruction/modeling through cardiac cycle

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

4  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 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.

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

8  Represent the contour  Polar coordinates  Heart contour  Reflectivity

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

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