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Anna Salvatella, & Maria Vanrell. (2002). Towards a texture representation database.
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Maria Vanrell. (1997). Exploring the space of behaviour of a texture perception algorithm.
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Mathieu Nicolas Delalandre, Ernest Valveny, & Josep Llados. (2008). Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview.
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Alicia Fornes. (2005). Analysis of Old Handwritten Musical Scores.
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Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2007). A New Optimal Algorithm for the Generalized Median Graph Computation Based on the Maximum Common Subgraph.
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Josep Llados. (1996). Interpretacio de dibuixos linials fets a ma alçada mitjançant isomorfisme entre subgrafs i transformacio de Hough.
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Joan Mas. (2005). Syntactic approaches to recognize bi-dimensional shapes in graphics recognition. Application to sketching interfaces.
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X. Orriols, Andrew Willis, X. Binefa, & David B. Cooper. (2000). Bayesian estimation of axial symmetries from partial data, a generative model approach.
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Oriol Ramos Terrades. (2003). Descripcio i classificacio de simbols tecnics usant la transformada de crestetes.
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Marçal Rusiñol. (2006). A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents.
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Jaume Rodriguez, S. Yacoub, Gemma Sanchez, & Josep Llados. (2006). Performance Evaluation, Comparison and Combination of Commercial Handwriting Recognition Engines.
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Gemma Sanchez, Josep Llados, & K. Tombre. (2000). A mean string algorithm to compute the average among a set of 2D shapes.
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Eric Amiel. (2005). Visualisation de vaisseaux sanguins (Enric Marti, Ed.). Bachelor's thesis, Université Paul Sabatier Toulouse III, Toulouse.
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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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Jorge Bernal. (2009). Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction (Vol. 141). Master's thesis, , Barcelona, Spain.
Abstract: One of the biggest drawbacks related to the use of CT scanners is the cost (in memory and in time) associated. In this project many methods to simulate their functioning, but in a more feasible way (taking an industrial point of view), will be studied.
The main group of techniques that are being used are the one entitled as ’back-projection’. The concept behind is to simulate the X ray emission in CT scans by lines that cross with the image we want to reconstruct.
In the first part of this document euclidean geometry is used to face the tasks of projec- tion and back-projection. After analysing the results achieved it has been proved that this approach does not lead to a fully perfect reconstruction (and also has some other problems related to running time and memory cost). Because of this in the second part of the document ’Filtered Back-projection’ method is introduced in order to improve the results.
Filtered Back-projection methods rely on mathematical transforms (Fourier, Radon) in order to provide more accurate results that can be obtained in much less time. The main cause of this better results is the use of a filtering process before the back-projection in order to avoid high frequency-caused errors.
As a result of this project two different implementations (one for each approach) had been implemented in order to compare their performance.
Keywords: Projection, Back-projection, CT scan, Euclidean geometry, Radon transform
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