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Ferran Poveda |
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Title |
Visualització i interpretació tridimensional de l’arquitectura de les fibres musculars del miocardi |
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Report |
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2009 |
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Master en Tecnologies Multimedia |
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Master's thesis |
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08193 Bellaterra, Barcelona (Spain) |
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IAM; |
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IAM @ iam @ Pov2009 |
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1625 |
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Author |
Ferran Poveda |
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Title |
Computer Graphics and Vision Techniques for the Study of the Muscular Fiber Architecture of the Myocardium |
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Year |
2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Debora Gil;Enric Marti |
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IAM |
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Admin @ si @ Pov2013 |
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2417 |
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Author |
Fernando Vilariño; Enric Marti |
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Title |
New didactic techniques in the EHES applying mobile technologies |
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Miscellaneous |
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2008 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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IAM @ iam @ VIM2008 |
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1664 |
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Author |
Fernando Vilariño; Debora Gil; Petia Radeva |
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Title |
A Novel FLDA Formulation for Numerical Stability Analysis |
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2004 |
Publication |
Recent Advances in Artificial Intelligence Research and Development |
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113 |
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77-84 |
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Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision |
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Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision. |
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IOS Press |
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J. Vitrià, P. Radeva and I. Aguiló |
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978-1-58603-466-5 |
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MV;IAM;MILAB;SIAI |
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IAM @ iam @ VGR2004 |
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1663 |
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Author |
F.Guirado; Ana Ripoll; C.Roig; Aura Hernandez-Sabate; Emilio Luque |
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Title |
Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications |
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Book Chapter |
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2006 |
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Euro-Par 2006 Parallel Processing |
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LNCS |
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4128 |
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1095-1105 |
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12th International Euro–Par Conference |
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There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when execu- ting them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodo- logy we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate. |
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Springer-Verlag Berlin Heidelberg |
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Dresden, Germany (European Union) |
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UAB; W, E.N.; et al. |
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Lecture Notes In Computer Science |
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Euro–Par |
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IAM |
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IAM @ iam @ GRR2006a |
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1542 |
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Author |
F. Javier Sanchez; Jordi Vitria; Enric Marti |
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Title |
Transformaciones Morfológicas de Polígonos Isotéticos |
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Conference Article |
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Year |
1991 |
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Primer Congreso Español de Informática Gráfica. |
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OR;IAM;MV |
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IAM @ iam @ SVM1991 |
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1648 |
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Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
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Title |
Localizing Pulmonary Lesions Using Fuzzy Deep Learning |
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Conference Article |
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2019 |
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21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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290-294 |
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The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. |
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Timisoara; Rumania; September 2019 |
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SYNASC |
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IAM; 600.145; 600.140; 601.337; 601.323 |
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Admin @ si @ RSG2019 |
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3531 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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BronchoX: bronchoscopy exploration software for biopsy intervention planning |
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2018 |
Publication |
Healthcare Technology Letters |
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HTL |
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5 |
Issue |
5 |
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177–182 |
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Abstract |
Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea. |
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IAM; 600.096; 600.075; 601.323; 601.337; 600.145 |
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Admin @ si @ RSB2018a |
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3132 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
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Conference Article |
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2018 |
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OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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11041 |
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Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
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Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
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Granada; September 2018 |
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MICCAIW |
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IAM; 600.096; 600.075; 601.323; 600.145 |
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Admin @ si @ RSB2018b |
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3137 |
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Author |
Ernest Valveny; Enric Marti |
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A model for image generation and symbol recognition through the deformation of lineal shapes |
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Journal Article |
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2003 |
Publication |
Pattern Recognition Letters |
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PRL |
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24 |
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15 |
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2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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