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Agnes Borras, & Josep Llados. (2008). A Multi-Scale Layout Descriptor Based on Delaunay Triangulation for Image Retrieval. In 3rd International Conference on Computer Vision Theory and Applications VISAPP (2) 2008 (Vol. 2, pp. 139–144).
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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).
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Fadi Dornaika, & Bogdan Raducanu. (2008). Constructing Panoramic Views Through Facial Gaze Tracking. In IEEE International Conference on Multimedia and Expo, (969–972).
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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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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).
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Pau Baiget, Eric Sommerlade, I. Reid, & Jordi Gonzalez. (2008). Finding Prototypes to Estimate Trajectory Development in Outdoor Scenarios. In First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, (27–34).
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Ognjen Rudovic, & Xavier Roca. (2008). Building Temporale Templates for Human Behaviour Classification. In First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, (79–88).
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Carles Fernandez, Pau Baiget, & Jordi Gonzalez. (2008). Cognitive-Guided Semantic Exploitation in Video Surveillance Interfaces. In First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences. BMVC 2008, (53–60).
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Eduard Vazquez, Joost Van de Weijer, & Ramon Baldrich. (2008). Image Segmentation in the Presence of Shadows and Highligts. In 10th European Conference on Computer Vision (Vol. 5305, 1–14). LNCS.
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Carme Julia, Angel Sappa, Felipe Lumbreras, & Antonio Lopez. (2008). Recovery of Surface Normals and Reflectance from Different Lighting Conditions. In 5th International Conference on Image Analysis and Recognition (Vol. 5112, 315–325). LNCS.
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Fadi Dornaika, & Angel Sappa. (2008). Real Time on Board Stereo Camera Pose through Image Registration. In IEEE Intelligent Vehicles Symposium, (804–809).
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Carme Julia, Angel Sappa, Felipe Lumbreras, & Joan Serrat. (2008). Photometric Stereo through and Adapted Alternation Approach. In IEEE International Conference on Image Processing, (1500–1503).
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Angel Sappa, Fadi Dornaika, David Geronimo, & Antonio Lopez. (2008). Registration-based Moving Object Detection from a Moving Camera. In IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles (65–69).
Abstract: This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach.
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C. Santa-Marta, Jaume Garcia, A. Bajo, J.J. Vaquero, M. Ledesma-Carbayo, & Debora Gil. (2008). Influence of the Temporal Resolution on the Quantification of Displacement Fields in Cardiac Magnetic Resonance Tagged Images. In S. A. Roberto hornero (Ed.), XXVI Congreso Anual de la Sociedad Española de Ingenieria Biomedica (352–353).
Abstract: It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possibl e. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%.
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Carlo Gatta, Oriol Pujol, O. Rodriguez-Leor, Josefina Mauri, & Petia Radeva. (2008). Improved Rigid Registration of Vessel Structures using the Fast Radial Symmetry Transform. In Computer Vision for Intravascular Imaging CVII’08 Workshop Medical Image Computing and Computer–Assisted Intervention , 11th International Conference (128–136).
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