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David Roche, Debora Gil, & Jesus Giraldo. (2011). "An inference model for analyzing termination conditions of Evolutionary Algorithms " In 14th Congrès Català en Intel·ligencia Artificial (pp. 216–225).
Abstract: In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution.
Keywords: Evolutionary Computation Convergence, Termination Conditions, Statistical Inference
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Sergio Vera, Debora Gil, Antonio Lopez, & Miguel Angel Gonzalez Ballester. (2012). "Multilocal Creaseness Measure " . The Insight Journal, .
Abstract: This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.
Keywords: Ridges, Valley, Creaseness, Structure Tensor, Skeleton,
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Fernando Vilariño, & Enric Marti. (2008)." New didactic techniques in the EHES applying mobile technologies" .
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Petia Radeva, & Enric Marti. (1995). "Facial Features Segmentation by Model-Based Snakes. ".
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Jaume Garcia. (2002)." Propagacio de fronts per a la segmentacio en imatges IVUS" .
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Albert Andaluz. (2009). "LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction " (Vol. 142). Master's thesis, , Bellaterra 08193, Barcelona, Spain.
Abstract: The Diagnosis of Left Ventricle (LV) pathologies is related to regional wall motion analysis. Health indicator scores such as the rotation and the torsion are useful for the diagnose of the Left Ventricle (LV) function. However, this requires proper identification of LV segments. On one hand, manual segmentation is robust, but it is slow and requires medical expertise. On the other hand, the tag pattern in Tagged Magnetic Resonance (TMR) sequences is a problem for the automatic segmentation of the LV boundaries. Consequently, we propose a method based in the classical formulation of parametric Snakes, combined with Active Shape models. Our semantic definition of the LV is tagged tissue that experiences motion in the systolic cycle. This defines two energy potentials for the Snake convergence. Additionally, the mean shape corrects excessive deviation from the anatomical shape. We have validated our approach in 15 healthy volunteers and two short axis cuts. In this way, we have compared the automatic segmentations to manual shapes outlined by medical experts. Also, we have explored the accuracy of clinical scores computed using automatic contours. The results show minor divergence in the approximation and the manual segmentations as well as robust computation of clinical scores in all cases. From this we conclude that the proposed method is a promising support tool for clinical analysis.
Keywords: Active Contour Models; Snakes; Active Shape Models; Deformable Templates; Left Ventricle Segmentation; Generalized Orthogonal Procrustes Analysis; Harmonic Phase Flow; Principal Component Analysis; Tagged Magnetic Resonance
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Oriol Rodriguez-Leon, Josefina Mauri, Eduard Fernandez-Nofrerias, M.Gomez, Antonio Tovar, L.Cano, et al. (2002)." Ecografia Intracoronaria: Segmentacio Automatica de area de la llum" . Revista Societat Catalana de Cardiologia, 4(4), 42.
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Debora Gil, & Petia Radeva. (2004). "A Regularized Curvature Flow Designed for a Selective Shape Restoration " . IEEE Transactions on Image Processing, 13, 1444–1458.
Abstract: Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.
Keywords: Geometric flows, nonlinear filtering, shape recovery.
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Oriol Rodriguez-Leon, Josefina Mauri, Eduard Fernandez-Nofrerias, Antonio Tovar, Vicente del Valle, Aura Hernandez-Sabate, et al. (2004)." Utilizacion de la estructura de los campos vectoriales para la deteccion de la Adventicia en imagenes de Ecografia Intracoronaria" . Revista Española de Cardiología, 57(2), 100.
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Joel Barajas, Jaume Garcia, Francesc Carreras, Sandra Pujades, & Petia Radeva. (2005). "Angle Images Using Gabor Filters in Cardiac Tagged MRI " In Proceeding of the 2005 conference on Artificial Intelligence Research and Development (pp. 107–114). Amsterdam, The Netherlands: IOS Press.
Abstract: Tagged Magnetic Resonance Imaging (MRI) is a non-invasive technique used to examine cardiac deformation in vivo. An Angle Image is a representation of a Tagged MRI which recovers the relative position of the tissue respect to the distorted tags. Thus cardiac deformation can be estimated. This paper describes a new approach to generate Angle Images using a bank of Gabor filters in short axis cardiac Tagged MRI. Our method improves the Angle Images obtained by global techniques, like HARP, with a local frequency analysis. We propose to use the phase response of a combination of a Gabor filters bank, and use it to find a more precise deformation of the left ventricle. We demonstrate the accuracy of our method over HARP by several experimental results.
Keywords: Angle Images, Gabor Filters, Harp, Tagged Mri
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