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Author |
Jaume Garcia |
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Title |
Generalized Active Shape Models Applied to Cardiac Function Analysis |
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Report |
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2004 |
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CVC Technical Report |
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78 |
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Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography. |
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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. |
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CVC (UAB) |
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Master's thesis |
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IAM @ iam @ Gar2004 |
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1513 |
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Author |
Debora Gil |
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Title |
Geometric Differential Operators for Shape Modelling |
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Year |
2004 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Medical imaging feeds research in many computer vision and image processing fields: image filtering, segmentation, shape recovery, registration, retrieval and pattern matching. Because of their low contrast changes and large variety of artifacts and noise, medical imaging processing techniques relying on an analysis of the geometry of image level sets rather than on intensity values result in more robust treatment. From the starting point of treatment of intravascular images, this PhD thesis ad- dresses the design of differential image operators based on geometric principles for a robust shape modelling and restoration. Among all fields applying shape recovery, we approach filtering and segmentation of image objects. For a successful use in real images, the segmentation process should go through three stages: noise removing, shape modelling and shape recovery. This PhD addresses all three topics, but for the sake of algorithms as automated as possible, techniques for image processing will be designed to satisfy three main principles: a) convergence of the iterative schemes to non-trivial states avoiding image degeneration to a constant image and representing smooth models of the originals; b) smooth asymptotic behav- ior ensuring stabilization of the iterative process; c) fixed parameter values ensuring equal (domain free) performance of the algorithms whatever initial images/shapes. Our geometric approach to the generic equations that model the different processes approached enables defining techniques satisfying all the former requirements. First, we introduce a new curvature-based geometric flow for image filtering achieving a good compromise between noise removing and resemblance to original images. Sec- ond, we describe a new family of diffusion operators that restrict their scope to image level curves and serve to restore smooth closed models from unconnected sets of points. Finally, we design a regularization of snake (distance) maps that ensures its smooth convergence towards any closed shape. Experiments show that performance of the techniques proposed overpasses that of state-of-the-art algorithms. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona (Spain) |
Editor |
Jordi Saludes i Closa;Petia Radeva |
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84-933652-0-3 |
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prit |
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IAM; |
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IAM @ iam @ GIL2004 |
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1517 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Shape Restoration via a Regularized Curvature Flow |
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Journal Article |
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Year |
2004 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
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Volume |
21 |
Issue |
3 |
Pages |
205-223 |
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Abstract |
Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes.
In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications. |
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IAM;MILAB |
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IAM @ iam @ GiR2004c |
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1532 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Inhibition of False Landmarks |
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Book Chapter |
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Year |
2004 |
Publication |
Recent Advances in Artificial Intelligence Research and Development |
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233-244 |
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We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Its high sensitivity to changes in vector directions makes it suitable for landmark location in real images prone to need smoothing to reduce the impact of noise. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our Inhibition Orientation Energy (IOE) landmark locator. |
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IOS Press |
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Barcelona (Spain) |
Editor |
al, J.V. et |
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IAM;MILAB |
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IAM @ iam @ GiR2004a |
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1533 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva; E.N.Nofrerias |
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Title |
Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images |
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Conference Article |
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Year |
2004 |
Publication |
Proc. Computers in Cardiology |
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31 |
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229-232 |
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The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles |
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Chicago (USA) |
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IAM; MILAB |
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IAM @ iam @ HGR2004 |
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1555 |
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Author |
Oriol Rodriguez-Leon; Josefina Mauri;Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
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Title |
Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria |
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Journal Article |
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2004 |
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Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología |
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57 |
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2 |
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100 |
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IAM;MILAB |
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no |
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IAM @ iam @ RMF2004 |
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1642 |
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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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Book Chapter |
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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 |
Joel Barajas; Jaume Garcia; Francesc Carreras; Sandra Pujades; Petia Radeva |
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Title |
Angle Images Using Gabor Filters in Cardiac Tagged MRI |
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Conference Article |
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2005 |
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Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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107-114 |
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Angle Images, Gabor Filters, Harp, Tagged Mri |
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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. |
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Amsterdam; The Netherlands |
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IOS Press |
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Amsterdam, The Netherlands |
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1-58603-560-6 |
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CAIRD |
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IAM;MILAB |
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BCNPCL @ bcnpcl @ BGC2005; IAM @ iam |
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595 |
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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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Year |
2005 |
Publication |
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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Author |
Debora Gil; Aura Hernandez-Sabate; Antoni Carol; Oriol Rodriguez; Petia Radeva |
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Title |
A Deterministic-Statistic Adventitia Detection in IVUS Images |
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Conference Article |
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2005 |
Publication |
ESC Congress |
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Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation |
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Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles. |
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Stockholm; Sweden; September 2005 |
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IAM @ iam @ RMF2005a |
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1523 |
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