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Jaume Garcia. (2004). Generalized Active Shape Models Applied to Cardiac Function Analysis. Master's thesis, , .
Abstract: Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown.
Keywords: Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography.
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Jaume Garcia, Petia Radeva, & Francesc Carreras. (2004). Combining Spectral and Active Shape methods to Track Tagged MRI. In Recent Advances in Artificial Intelligence Research and Development (pp. 37–44). IOS Press.
Abstract: 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.
Keywords: MR; tagged MR; ASM; LV segmentation; motion estimation.
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Jian Yang, Zhong Jin, Jing-Yu Yang, David Zhang, & Alejandro F. Frangi. (2004). Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognition, 37(10): 2097–2100 (IF: 2.176).
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Jordi Gonzalez. (2004). Human Sequence Evaluation: the Key-frame Approach (Xavier Roca, & Javier Varona, Eds.). Ph.D. thesis, , .
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2004). Situation Graph Trees for Human Behavior Modeling.
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2004). Analysis of Human Walking Based on aSpaces.
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Jordi Vitria, Petia Radeva, & I. Aguilo. (2004). Recent Advances in Artificial Intelligence Research and Development. In Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9.
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Josep Llados, & Gemma Sanchez. (2004). Graph Matching vs. Graph Parsing in Graphics Recognition: A Combined Approach. IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, 455–473.
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Josep Llados, & Young-Bin Kwon. (2004). Graphics Recognition. Recent Advances and Perspectives.
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M. Bressan, David Guillamet, & Jordi Vitria. (2004). Multiclass Object Recognition using Class-Conditional Independent Component Analisis. Cybernetics and Systems, 35/1:35–61 (IF: 0.768).
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M.A. Garcia, & Angel Sappa. (2004). Efficient Generation of Discontinuity-Preserving Adaptive Triangulations from Range Images. IEEE Trans. on Systems, Man, and Cybernetics (Part B), 34(5):2003–2014 (IF: 1.052).
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Maria Vanrell, Ramon Baldrich, Anna Salvatella, Robert Benavente, & Francesc Tous. (2004). Induction operators for a computational colour-texture representation. Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651).
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Maya Dimitrova, I. Terziev, Petia Radeva, & Juan J. Villanueva. (2004). Java-Servlet Technology for Building New Web Document Classifiers.
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Maya Dimitrova, Petia Radeva, David Rotger, D. Boyadjiev, & Juan J. Villanueva. (2004). Advanced Cardiological Diagnosis via Intelligent Image Analysis.
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Misael Rosales, Petia Radeva, J. Mauri, & Oriol Pujol. (2004). Simulation Model of Intravascular Ultrasound Images.
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