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Author  |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Embedding of Graphs with Discrete Attributes Via Label Frequencies |
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Journal Article |
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2013 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
Abbreviated Journal |
IJPRAI |
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Volume |
27 |
Issue |
3 |
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1360002-1360029 |
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Keywords |
Discrete attributed graphs; graph embedding; graph classification |
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Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. |
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Admin @ si @ GVB2013 |
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2305 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
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3072-3083 |
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Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
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Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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0031-3203 |
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Admin @ si @ GVB2012a |
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1992 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
33 |
Issue |
15 |
Pages |
1980–1990 |
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Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
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Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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Admin @ si @ GVB2012b |
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1993 |
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Author |
Jaume Gibert; Ernest Valveny |
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Title |
Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. |
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Conference Article |
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Year |
2010 |
Publication |
13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition |
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6218 |
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223–232 |
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Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster. |
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Springer Berlin Heidelberg |
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In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano, |
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0302-9743 |
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978-3-642-14979-5 |
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S+SSPR |
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DAG @ dag @ GiV2010 |
Serial |
1416 |
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Author |
Jaume Gibert |
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Title |
Vector Space Embedding of Graphs via Statistics of Labelling Information |
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Book Whole |
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Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.
Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.
In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Ernest Valveny |
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Admin @ si @ Gib2012 |
Serial |
2204 |
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Author |
Jaume Gibert |
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Title |
Learning structural representations and graph matching paradigms in the context of object recognition |
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Report |
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2009 |
Publication |
CVC Technical Report |
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143 |
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Computer Vision Center |
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Master's thesis |
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Admin @ si @ Gib2009 |
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2397 |
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Author |
Jaume Garcia; Petia Radeva; Francesc Carreras |
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Title |
Combining Spectral and Active Shape methods to Track Tagged MRI |
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2004 |
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Recent Advances in Artificial Intelligence Research and Development |
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37-44 |
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MR; tagged MR; ASM; LV segmentation; motion estimation. |
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Tagged magnetic resonance is a very usefull and unique tool that provides a complete local and global knowledge of the left ventricle (LV) motion. In this article we introduce a method capable of tracking and segmenting the LV. Spectral methods are applied in order to obtain the so called HARP images which encode information about movement and are the base for LV point-tracking. For segmentation we use Active Shapes (ASM) that model LV shape variation in order to overcome possible local misplacements of the boundary. We finally show experiments on both synthetic and real data which appear to be very promising. |
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IOS Press |
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CCIA |
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IAM;MILAB |
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IAM @ iam @ GRC2004 |
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1488 |
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Author |
Jaume Garcia; Joel Barajas; Francesc Carreras; Sandra Pujades; Petia Radeva |
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Title |
An intuitive validation technique to compare local versus global tagged MRI analysis |
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Conference Article |
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2005 |
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Computers In Cardiology |
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32 |
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29–32 |
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Myocardium appears as a uniform tissue that seen in convectional Magnetic Resonance Images (MRI) shows just the contractile part of its movement. MR Tagging is a unique imaging technique that prints a grid over the tissue which moves according to the underlying movement of the myocardium revealing the true deformation of the cardiac muscle. Optical flow techniques based on spectral information estimate tissue displacement by analyzing information encoded in the phase maps which can be obtained using, local (Gabor) and global (HARP) methods. In this paper we compare both in synthetic and real Tagged MR sequences. We conclude that local method is slightly more accurate than the global one. On the other hand, global method is more efficient as it is much faster and less parameters have to be taken into account |
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Lyon (France) |
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0-7803-9337-6 |
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IAM;MILAB |
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IAM @ iam @ GBC2005 |
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639 |
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Jaume Garcia; Francesc Carreras; Sandra Pujades; Debora Gil |
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Title |
Regional motion patterns for the Left Ventricle function assessment |
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Conference Article |
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2008 |
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Proc. 19th Int. Conf. Pattern Recognition ICPR 2008 |
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1-4 |
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Regional scores (e.g. strain, perfusion) of the Left Ventricle (LV) functionality are playing an increasing role in the diagnosis of cardiac diseases. A main limitation is the lack of normality models for complementary scores oriented to assessment of the LV integrity. This paper introduces an original framework based on a parametrization of the LV domain, which allows comparison across subjects of local physiological measures of different nature. We compute regional normality patterns in a feature space characterizing the LV function. We show the consistency of the model for the regional motion on healthy and hypokinetic pathological cases |
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IAM @ iam @ GCP2008 |
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1510 |
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Jaume Garcia; Debora Gil; Sandra Pujades; Francesc Carreras |
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Title |
Valoracion de la Funcion del Ventriculo Izquierdo mediante Modelos Regionales Hiperparametricos |
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Journal Article |
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2008 |
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Revista Española de Cardiologia |
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61 |
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3 |
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79 |
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La mayoría de la enfermedades cardiovasculares afectan a las propiedades contráctiles de la banda ventricular helicoidal. Esto se refleja en una variación del comportamiento normal de la función ventricular. Parámetros locales tales como los strains, o la deformación experimentada por el tejido, son indicadores capaces de detectar anomalías funcionales en territorios específicos. A menudo, dichos parámetros son considerados de forma separada. En este trabajo presentamos un marco computacional (el Dominio Paramétrico Normalizado, DPN) que permite integrarlos en hiperparámetros funcionales y estudiar sus rangos de normalidad. Dichos rangos permiten valorar de forma objetiva la función regional de cualquier nuevo paciente. Para ello, consideramos secuencias de resonancia magnética etiquetada a nivel basal, medio y apical. Los hiperparámetros se obtienen a partir del movimiento intramural del VI estimado mediante el método Harmonic Phase Flow. El DPN se define a partir de en una parametrización del Ventrículo Izquierdo (VI) en sus coordenadas radiales y circunferencial basada en criterios anatómicos. El paso de los hiperparámetros al DPN hace posible la comparación entre distintos pacientes. Los rangos de normalidad se definen mediante análisis estadístico de valores de voluntarios sanos en 45 regiones del DPN a lo largo de 9 fases sistólicas. Se ha usado un conjunto de 19 (14 H; E: 30.7±7.5) voluntarios sanos para crear los patrones de normalidad y se han validado usando 2 controles sanos y 3 pacientes afectados de contractilidad global reducida. Para los controles los resultados regionales se han ajustado dentro de la normalidad, mientras que para los pacientes se han obtenido valores anormales en las zonas descritas, localizando y cuantificando así el diagnóstico empírico. |
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IAM; |
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IAM @ iam @ GRP2008 |
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1032 |
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Jaume Garcia; Debora Gil; Sandra Pujades; Francesc Carreras |
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Title |
A Variational Framework for Assessment of the Left Ventricle Motion |
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Journal Article |
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2008 |
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International Journal Mathematical Modelling of Natural Phenomena |
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3 |
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6 |
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76-100 |
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Key words: Left Ventricle Dynamics, Ventricular Torsion, Tagged Magnetic Resonance, Motion Tracking, Variational Framework, Gabor Transform. |
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Impairment of left ventricular contractility due to cardiovascular diseases is reflected in left ventricle (LV) motion patterns. An abnormal change of torsion or long axis shortening LV values can help with the diagnosis and follow-up of LV dysfunction. Tagged Magnetic Resonance (TMR) is a widely spread medical imaging modality that allows estimation of the myocardial tissue local deformation. In this work, we introduce a novel variational framework for extracting the left ventricle dynamics from TMR sequences. A bi-dimensional representation space of TMR images given by Gabor filter banks is defined. Tracking of the phases of the Gabor response is combined using a variational framework which regularizes the deformation field just at areas where the Gabor amplitude drops, while restoring the underlying motion otherwise. The clinical applicability of the proposed method is illustrated by extracting normality models of the ventricular torsion from 19 healthy subjects. |
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IAM @ iam @ GGC2008a |
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1058 |
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Author |
Jaume Garcia; Debora Gil; Luis Badiella; Aura Hernandez-Sabate; Francesc Carreras; Sandra Pujades; Enric Marti |
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A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function |
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2010 |
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IEEE Transactions on Medical Imaging |
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TMI |
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29 |
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3 |
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733-745 |
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A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains. |
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0278-0062 |
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IAM @ iam @ GGH2010b |
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1507 |
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Jaume Garcia; Debora Gil; Joel Barajas; Francesc Carreras; Sandra Pujades; Petia Radeva |
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Characterization of ventricular torsion in healthy subjects using Gabor filters and a variational framework |
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2006 |
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Proc. Computers in Cardiology |
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877-880 |
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In this work, we present a fully automated method for tissue deformation estimation in tagged magnetic resonance images (TMRI). Gabor filter banks, tuned independently for each left ventricle level, provide optimally filtered complex images which phase remains constant along the cardiac cycle. This fact can be thought as the brightness constancy condition required by classical optical flow (OF) methods. Pairs of these filtered sequences, together with a variational formulation are used in a second step to obtain dense continuous deformation maps that we call Harmonic Phase Flow. This method has been used to determine reference values of ventricular torsion (VT) in a set of 8 healthy volunteers. The results encourage the use of VT as a useful parameter for ventricular function assessment in clinical routine. |
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IAM @ iam @ GGB2006a |
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1509 |
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Author |
Jaume Garcia; Debora Gil; Francesc Carreras; Sandra Pujades; R.Leta; Xavier Alomar; Guillem Pons-LLados |
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Title |
Patrons de Normalitat Regional per la Valoració de la Funció del Ventricle Esquerre |
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Conference Article |
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2008 |
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XX Congrés de la Societat Catalana de Cardiologia |
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60 |
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Les malalties cardiovasculars afecten les propietats contràctils de la banda ventricular i provoquen una variació de la funció del Ventricle Esquerre (VE) . Només els indicadors locals (strains, la deformació del teixit) són capaços de detectar anomalies en territoris específics del VE . Patrons de normalitat regionals d’aquests paràmetres serien d’utilitat a l’hora de valorar-ne la funció .
Presentem un Domini Paramètric Normalitzat (DPN) que permet comparar dades de diferents pacients i definir Patrons de Normalitat Regional (PNR) |
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Barcelona |
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catalan |
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catalan |
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IAM @ iam @ GGC2008b |
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1503 |
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Jaume Garcia; Debora Gil; Francesc Carreras; Sandra Pujades; R.Leta |
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Title |
Modelització 4-Dimensional de la Funció Siatólica del Ventricle Esquerre |
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Conference Article |
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2007 |
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XIX Congrés de la Societat Catalana de Cardiologia de Barcelona |
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133-134 |
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L’evolució tecnològica en el tractament de les imatges mèdiques permet reconstruir, amb el software apropiat, imatges tridimensionals de les estructures cardiovasculars i dotar-les de moviment. Les imatges 4D resultants faciliten l’estudi de la fisiopatologia de la insuficiència cardíaca en base als transtorns de l’activació electromecànica ventricular, el que pot ser d’interès en el procés de selecció de pacients candidats a teràpies de resincronització. Presentem els resultats preliminars de la reconstrucció 4D del ventricle esquerre (VE) a partir de les seqüències de tagging miocàrdic del VE. |
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IAM @ iam @ GGC2007 |
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1505 |
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