4,541 research outputs found
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Coupled PDEs for Non-Rigid Registration and Segmentation
In this paper we present coupled partial differential equations (PDEs) for the problem of joint segmentation and registration. The registration component of the method estimates a deformation field between boundaries of two structures. The desired coupling comes from two PDEs that estimate a common surface through segmentation and its non-rigid registration with a target image. The solutions of these two PDEs both decrease the total energy of the surface, and therefore aid each other in finding a locally optimal solution. Our technique differs from recently popular joint segmentation and registration algorithms, all of which assume a rigid transformation among shapes. We present both the theory and results that demonstrate the effectiveness of the approach
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Estimation of Vector Fields in Unconstrained and Inequality Constrained Variational Problems for Segmentation and Registration
Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between objects, and the contour or surface that defines the segmentation of the objects as well. We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and segmentation. We present both the theory and results that demonstrate our approach
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Active Polyhedron: Surface Evolution Theory Applied to Deformable Meshes
This paper presents a novel 3D deformable surface that we call an active polyhedron. Rooted in surface evolution theory, an active polyhedron is a polyhedral surface whose vertices deform to minimize a regional and/or boundarybased energy functional. Unlike continuous active surface models, the vertex motion of an active polyhedron is computed by integrating speed terms over polygonal faces of the surface. The resulting ordinary differential equations (ODEs) provide improved robustness to noise and allow for larger time steps compared to continuous active surfaces implemented with level set methods. We describe an electrostatic regularization technique that achieves global regularization while better preserving sharper local features. Experimental results demonstrate the effectiveness of an active polyhedron in solving segmentation problems as well as surface reconstruction from unorganized points
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Guidewire tracking in x-ray videos of endovascular interventions
We present a novel method to track a guidewire in cardiac xray video. Using variational calculus, we derive differential equations that deform a spline, subject to intrinsic and extrinsic forces, so that it matches the image data, remains smooth, and preserves an a priori length. We analytically derive these equations from first principles, and show how they include tangential terms, which we include in our model. To address the poor contrast often observed in x-ray video, we propose using phase congruency as an image-based feature. Experimental results demonstrate the success of the method in tracking guidewires in low contrast x-ray video
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Graph cuts segmentation using an elliptical shape prior
We present a graph cuts-based image segmentation technique that incorporates an elliptical shape prior. Inclusion of this shape constraint restricts the solution space of the segmentation result, increasing robustness to misleading information that results from noise, weak boundaries, and clutter. We argue that combining a shape prior with a graph cuts method suggests an iterative approach that updates an intermediate result to the desired solution. We first present the details of our method and then demonstrate its effectiveness in segmenting vessels and lymph nodes from pelvic magnetic resonance images, as well as human faces
A Variational Approach to the Evolution of Radial Basis Functions for Image Segmentation
In this paper we derive differential equations for evolving radial basis functions (RBFs) to solve segmentation problems. The differential equations result from applying variational calculus to energy functionals designed for image segmentation. Our methodology supports evolution of all parameters of each RBF, including its position, weight, orientation, and anisotropy, if present. Our framework is general and can be applied to numerous RBF interpolants. The resulting approach retains some of the ideal features of implicit active contours, like topological adaptivity, while requiring low storage overhead due to the sparsity of our representation, which is an unstructured list of RBFs. We present the theory behind our technique and demonstrate its usefulness for image segmentation
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A Contour-Based Approach to 3D Text Labeling on Triangulated Surfaces
This paper presents a simple and efficient method of forming a 3D text label on a 3D triangulated surface. The label is formed by projecting the 2D contours that define the text silhouette onto the triangulated surface, forming 3D contour paths. Surface polygons upon which the 3D contour paths lie are retriangulated using a novel approach that forms a polyline defining the region outside the contour. This algorithm produces labeled 3D surfaces that conform to the specifications of the STL format, making them suitable for fabrication by a rapid prototyping machine. We demonstrate the effectiveness of the algorithm in forming flat and extruded labels on non-trivial surfaces
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Information-theoretic feature detection in ultrasound images
The detection of image features is an essential component of medical image processing, and has wide-ranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in ultrasound images. Ultrasound images are corrupted by speckle noise, which is a disruptive random pattern that obscures the features of interest. Using theoretical probability density functions of the speckle intensity distributions, we derive analytic expressions that measure the distance between distributions taken from different regions in an ultrasound image and use these distances to detect features. We compare the technique to classic gradient-based feature detection methods
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Interacting Active Rectangles for Estimation of Intervertebral Disk Orientation
This paper presents a fast and efficient method to determine intervertebral disk orientation in a magnetic resonance (MR) image of the spine. The algorithm originates from active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. The method represents a vertebra as a rectangle, modeled as a semi-affine transformation applied to the unit square. A regional flow integrated along the rectangle's perimeter updates the rectangle's transformation to achieve the segmentation. Further constraints are added so that adjacent rectangles have similar orientation and scale. The orientation of a disk is then inferred from its adjacent vertebrae. Experiments show that the method is fast and effective in detecting the correct intervertebral disk orientation, which is used for transverse image planning
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Interacting Active Rectangles for Estimation of Intervertebral Disk Orientation
This paper presents a fast and efficient method to determine intervertebral disk orientation in a magnetic resonance (MR) image of the spine. The algorithm originates from active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. The method represents a vertebra as a rectangle, modeled as a semi-affine transformation applied to the unit square. A regional flow integrated along the rectangle's perimeter updates the rectangle's transformation to achieve the segmentation. Further constraints are added so that adjacent rectangles have similar orientation and scale. The orientation of a disk is then inferred from its adjacent vertebrae. Experiments show that the method is fast and effective in detecting the correct intervertebral disk orientation, which is used for transverse image planning
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