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Author |
Debora Gil; Petia Radeva; Josefina Mauri |
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Title |
Ivus Segmentation Via a Regularized Curvature Flow |
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Conference Article |
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2002 |
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X Congreso Anual de la Sociedad Española de Ingeniería Biomédica CASEIB 2002 |
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133-136 |
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Cardiac diseases are diagnosed and treated through a study of the morphology and dynamics of cardiac arteries. In- travascular Ultrasound (IVUS) imaging is of high interest to physicians since it provides both information. At the current state-of-the-art in image segmentation, a robust detection of the arterial lumen in IVUS demands manual intervention or ECG-gating. Manual intervention is a tedious and time consuming task that requires experienced observers, meanwhile ECG-gating is an acquisition technique not available in all clinical centers. We introduce a parametric algorithm that detects the arterial luminal border in in vivo sequences. The method consist in smoothing the sequences’ level surfaces under a regularized mean curvature flow that admits non-trivial steady states. The flow is based on a measure of the surface local smoothness that takes into account regularity of the surface curvature. |
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Saragossa, Espanya |
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IAM;MILAB |
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IAM @ iam @ GRM2002 |
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1536 |
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Author |
M.Gomez; Josefina Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leon; Carme Julia; Debora Gil; Petia Radeva |
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Title |
Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria |
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2002 |
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XXXVIII Congreso Nacional de la Sociedad Española de Cardiología. |
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IAM;ADAS;MILAB |
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IAM @ iam @ GMF2002d |
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1516 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Inhibition of false landmarks |
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Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
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PRL |
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27 |
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9 |
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1022-1030 |
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Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. 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. 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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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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IAM;MILAB |
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IAM @ iam @ GiR2006 |
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1529 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Extending anisotropic operators to recover smooth shapes |
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2005 |
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Computer Vision and Image Understanding |
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99 |
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1 |
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110-125 |
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Contour completion; Functional extension; Differential operators; Riemmanian manifolds; Snake segmentation |
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Anisotropic differential operators are widely used in image enhancement processes. Recently, their property of smoothly extending functions to the whole image domain has begun to be exploited. Strong ellipticity of differential operators is a requirement that ensures existence of a unique solution. This condition is too restrictive for operators designed to extend image level sets: their own functionality implies that they should restrict to some vector field. The diffusion tensor that defines the diffusion operator links anisotropic processes with Riemmanian manifolds. In this context, degeneracy implies restricting diffusion to the varieties generated by the vector fields of positive eigenvalues, provided that an integrability condition is satisfied. We will use that any smooth vector field fulfills this integrability requirement to design line connection algorithms for contour completion. As application we present a segmenting strategy that assures convergent snakes whatever the geometry of the object to be modelled is. |
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1077-3142 |
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IAM;MILAB |
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IAM @ iam @ GIR2005 |
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1530 |
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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 |
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Journal of Mathematical Imaging and Vision |
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21 |
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3 |
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205-223 |
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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) |
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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 |
Debora Gil; Petia Radeva |
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Title |
Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling |
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Book Chapter |
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Year |
2003 |
Publication |
Energy Minimization Methods In Computer Vision And Pattern Recognition |
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LNCS |
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2683 |
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357-372 |
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Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature |
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Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time. |
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Springer, Berlin |
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Lisbon, PORTUGAL |
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Springer, B. |
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Lecture Notes in Computer Science |
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LNCS |
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0302-9743 |
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3-540-40498-8 |
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IAM;MILAB |
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IAM @ iam @ GIR2003b |
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1535 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Curvature based Distance Maps |
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Report |
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2003 |
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CVC Technical Report |
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70 |
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Computer Vision Center |
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IAM;MILAB |
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IAM @ iam @ GIR2003a |
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1534 |
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Author |
Debora Gil |
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Title |
Geometric Differential Operators for Shape Modelling |
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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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Ediciones Graficas Rey |
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Barcelona (Spain) |
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Jordi Saludes i Closa;Petia Radeva |
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84-933652-0-3 |
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IAM; |
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IAM @ iam @ GIL2004 |
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1517 |
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Author |
Debora Gil |
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Title |
Regularized Curvature Flow |
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2002 |
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CVC Technical Report |
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63 |
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Computer Vision Centre |
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IAM @ iam @ Gil2002 |
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1518 |
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