|
Fadi Dornaika, & Bogdan Raducanu. (2008). Detecting and Tracking of 3D Face Pose for Human-Robot Interaction. In IEEE International Conference on Robotics and Automation, (1716–1721).
|
|
Fadi Dornaika, & Bogdan Raducanu. (2008). Constructing Panoramic Views Through Facial Gaze Tracking. In IEEE International Conference on Multimedia and Expo, (969–972).
|
|
Oriol Ramos Terrades, Ernest Valveny, & Salvatore Tabbone. (2008). On the Combination of Ridgelets Descriptors for Symbol Recognition. In Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113.
|
|
Ernest Valveny, Salvatore Tabbone, & Oriol Ramos Terrades. (2008). Performance Characterization of Shape Descriptors for Symbol Representation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 278–287). LNCS.
|
|
Ernest Valveny, Philippe Dosch, & Alicia Fornes. (2008). Report on the Third Contest on Symbol Recognition. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 321–328). LNCS.
|
|
Mathieu Nicolas Delalandre, Jean-Marc Ogier, & Josep Llados. (2008). A Fast Cbir System of Old Ornamental Letter. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Reognition: Recent Advances and New Opportunities (Vol. 5046, 135–144). LNCS.
|
|
Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Herve Locteau, & Eric Trupin. (2008). Building Synthetic Graphical Documents for Performance Evaluation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 288–298). LNCS.
|
|
Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez, & Joan Mas. (2008). Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 30–40). LNCS.
|
|
Alicia Fornes, Josep Llados, & Gemma Sanchez. (2008). Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 52–60). LNCS.
|
|
Joan Mas, J.A. Jorge, Gemma Sanchez, & Josep Llados. (2008). Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities, (Vol. 5046, 176–187). LNCS.
|
|
Jose Antonio Rodriguez, & Florent Perronnin. (2008). Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 188–198). LNCS.
|
Debora Gil, Jaume Garcia, Mariano Vazquez, Ruth Aris, & Guilleaume Houzeaux. (2008). Patient-Sensitive Anatomic and Functional 3D Model of the Left Ventricle Function. In 8th World Congress on Computational Mechanichs (WCCM8).
Abstract: Early diagnosis and accurate treatment of Left Ventricle (LV) dysfunction significantly increases the patient survival. Impairment of LV contractility due to cardiovascular diseases is reflected in its motion patterns. Recent advances in medical imaging, such as Magnetic Resonance (MR), have encouraged research on 3D simulation and modelling of the LV dynamics. Most of the existing 3D models [1] consider just the gross anatomy of the LV and restore a truncated ellipse which deforms along the cardiac cycle. The contraction mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fibers. It follows that such simplified models do not allow evaluation of the heart electro-mechanical function and coupling, which has recently risen as the key point for understanding the LV functionality [2]. In order to thoroughly understand the LV mechanics it is necessary to consider the complete anatomy of the LV given by the orientation of the myocardial fibres in 3D space as described by Torrent Guasp [3].
We propose developing a 3D patient-sensitive model of the LV integrating, for the first time, the ven- tricular band anatomy (fibers orientation), the LV gross anatomy and its functionality. Such model will represent the LV function as a natural consequence of its own ventricular band anatomy. This might be decisive in restoring a proper LV contraction in patients undergoing pace marker treatment.
The LV function is defined as soon as the propagation of the contractile electromechanical pulse has been modelled. In our experiments we have used the wave equation for the propagation of the electric pulse. The electromechanical wave moves on the myocardial surface and should have a conductivity tensor oriented along the muscular fibers. Thus, whatever mathematical model for electric pulse propa- gation [4] we consider, the complete anatomy of the LV should be extracted.
The LV gross anatomy is obtained by processing multi slice MR images recorded for each patient. Information about the myocardial fibers distribution can only be extracted by Diffusion Tensor Imag- ing (DTI), which can not provide in vivo information for each patient. As a first approach, we have
Figure 1: Scheme for the Left Ventricle Patient-Sensitive Model.
computed an average model of fibers from several DTI studies of canine hearts. This rough anatomy is the input for our electro-mechanical propagation model simulating LV dynamics. The average fiber orientation is updated until the simulated LV motion agrees with the experimental evidence provided by the LV motion observed in tagged MR (TMR) sequences. Experimental LV motion is recovered by applying image processing, differential geometry and interpolation techniques to 2D TMR slices [5]. The pipeline in figure 1 outlines the interaction between simulations and experimental data leading to our patient-tailored model.
Keywords: Left Ventricle, Electromechanical Models, Image Processing, Magnetic Resonance.
|
|
Joan Marti, Jose Miguel Benedi, Ana Maria Mendonça, & Joan Serrat. (2007). Pattern Recognition and Image Analysis (Vol. 6669). LNCS.
|
|
David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Face Verification Sharing Knowledge from Different Subjects. In 2nd International Conference on Computer Vision Theory and Applications (Vol. 2, 268–289).
|
|
Agata Lapedriza, Santiago Segui, David Masip, & Jordi Vitria. (2008). A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning. Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications,, 299–308.
|
