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Misael Rosales, Petia Radeva, Oriol Rodriguez, & Debora Gil. (2005). "Suppression of IVUS Image Rotation. A Kinematic Approach " In Monica Andres and Hernandez Petia and Santos A. and R. Frangi (Ed.), Functional Imaging and Modeling of the Heart (Vol. 3504, pp. 889–892). Lecture Notes in Computer Science, 3504. Springer Berlin / Heidelberg.
Abstract: IntraVascular Ultrasound (IVUS) is an exploratory technique used in interventional procedures that shows cross section images of arteries and provides qualitative information about the causes and severity of the arterial lumen narrowing. Cross section analysis as well as visualization of plaque extension in a vessel segment during the catheter imaging pullback are the technique main advantages. However, IVUS sequence exhibits a periodic rotation artifact that makes difficult the longitudinal lesion inspection and hinders any segmentation algorithm. In this paper we propose a new kinematic method to estimate and remove the image rotation of IVUS images sequences. Results on several IVUS sequences show good results and prompt some of the clinical applications to vessel dynamics study, and relation to vessel pathology.
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Misael Rosales, Petia Radeva, Oriol Rodriguez-Leor, & Debora Gil. (2009). "Modelling of image-catheter motion for 3-D IVUS " . Medical image analysis, 13(1), 91–104.
Abstract: Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS.
Keywords: Intravascular ultrasound (IVUS); Motion estimation; Motion decomposition; Fourier
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David Rotger, Misael Rosales, Jaume Garcia, Oriol Pujol, Josefina Mauri, & Petia Radeva. (2003). "Active Vessel: A New Multimedia Workstation for Intravascular Ultrasound and Angiography Fusion " . Computers in Cardiology, 30, 65–68.
Abstract: AcriveVessel is a new multimedia workstation which enables the visualization, acquisition and handling of both image modalities, on- and ofline. It enables DICOM v3.0 decompression and browsing, video acquisition,repmduction and storage for IntraVascular UltraSound (IVUS) and angiograms with their corresponding ECG,automatic catheter segmentation in angiography images (using fast marching algorithm). BSpline models definition for vessel layers on IVUS images sequence and an extensively validated tool to fuse information. This approach defines the correspondence of every IVUS image with its correspondent point in the angiogram and viceversa. The 3 0 reconstruction of the NUS catheterhessel enables real distance measurements as well as threedimensional visualization showing vessel tortuosity in the space.
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F. Javier Sanchez, Jordi Vitria, & Enric Marti. (1991)." Transformaciones Morfológicas de Polígonos Isotéticos" In Primer Congreso Español de Informática Gráfica..
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Gemma Sanchez, Josep Llados, & Enric Marti. (1997). Segmentation and analysis of linial texture in plans In Intelligence Artificielle et Complexité.. Paris.
Abstract: The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
Keywords: Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
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Gemma Sanchez, Ernest Valveny, Josep Llados, Enric Marti, Oriol Ramos Terrades, N.Lozano, et al. (2003)." A system for virtual prototyping of architectural projects" In Proceedings of Fifth IAPR International Workshop on Pattern Recognition (pp. 65–74).
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Joan Serrat, & Enric Marti. (1991)." Elastic matching using interpolation splines" In IV Spanish Symposium of Pattern Recognition and image Analysis.
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Elena Valderrama, Joan Oliver, Josep Maria-Basart, Enric Marti, Petia Radeva, Ricardo Toledo, et al. (2005)." Convergencia al EEES de la ingeniería informática. Título de Grado en tecnología (Informática)" .
Abstract: Elena Valderrama
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Ernest Valveny, & Enric Marti. (2001). "Learning of structural descriptions of graphic symbols using deformable template matching " In Proc. Sixth Int Document Analysis and Recognition Conf (pp. 455–459).
Abstract: Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.
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Ernest Valveny, & Enric Marti. (2000). "Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition " . Graphics Recognition Recent Advances, 1941, 193–208.
Abstract: We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.
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