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Murad Al Haj. (2008). Face Detection in Color Images Using Primitive Shape Features.
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Mathieu Nicolas Delalandre, Ernest Valveny, & Josep Llados. (2008). Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview.
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H.M.G. Stokman, & Theo Gevers. (2007). Selection and Fusion of Color Models for Image Feature Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.29(3):371–381.
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Joost Van de Weijer, Theo Gevers, & A. Gijsenij. (2007). Edge-Based Color Constancy. IEEE Trans. on Image Processing, vol. 16(9):2207–2214.
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Sergio Escalera, David M.J. Tax, Oriol Pujol, Petia Radeva, & Robert P.W. Duin. (2008). Subclass Problem-Dependent Design for Error-Correcting Output Codes. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054.
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S. Tanimoto, N. Bruining, David Rotger, Petia Radeva, J. Ligthart, R.T. van Domburg, et al. (2008). Late Stent Recoil of the Bioabsorbable Everolimus Eluting Coronary Stent and its Relationship with Stent Struts Distribution and Plaque Morphology. Journal of the American College of Cardiology, vol. 52(20):1616–1620.
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Miquel Ferrer, Robert Benavente, Ernest Valveny, J. Garcia, Agata Lapedriza, & Gemma Sanchez. (2008). Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Detection of Complex Salient Regions. EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages.
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva, & Tian Qi. (2008). Distance Learning for Similarity Estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462.
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Mikhail Mozerov, Ariel Amato, Xavier Roca, & Jordi Gonzalez. (2008). Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering. Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665.
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Jose Manuel Alvarez, Felipe Lumbreras, Antonio Lopez, & Theo Gevers. (2012). Understanding Road Scenes using Visual Cues.
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Matthias S. Keil, Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine. PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590.
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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.
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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.
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X. Varona, Jordi Gonzalez, Ignasi Rius, & Juan J. Villanueva. (2008). Importance of Detection for Video Surveillance Applications. Optical Engineering, vol. 47(8), 087201/1–9.
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