53 research outputs found
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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3D ball skinning using PDEs for generation of smooth tubular surfaces
We present an approach to compute a smooth, interpolating skin of an ordered set of 3D balls. By construction, the skin is constrained to be C-1 continuous, and for each ball, it is tangent to the ball along a circle of contact. Using an energy formulation, we derive differential equations that are designed to minimize the skin's surface area, mean curvature, or convex combination of both. Given an initial skin, we update the skin's parametric representation using the differential equations until convergence occurs. We demonstrate the method's usefulness in generating interpolating skins of balls of different sizes and in various configurations
3D ball skinning using PDEs for generation of smooth tubular surfaces
We present an approach to compute a smooth, interpolating skin of an ordered set of
3D balls. By construction, the skin is constrained to be C1 continuous, and for each
ball, it is tangent to the ball along a circle of contact. Using an energy formulation,
we derive differential equations that are designed to minimize the skin’s surface area,
mean curvature, or convex combination of both. Given an initial skin, we update the
skin’s parametric representation using the differential equations until convergence
occurs. We demonstrate the method’s usefulness in generating interpolating skins
of balls of different sizes and in various configurations
Endovaskuler müdahalelerde x-ray videodan kılavuz teli izleme = Guidewire tracking in x-ray videos of endovascular interventions
Bu bildiride kalp x-ray videolarında kılavuz telinin izlenmesi
ic¸in yeni bir metot sunulmaktadır. Değgişimler hesabı kullanılarak
bir kobra eğrisini içkin ve dıştan gelen kuvvetler
ile kısıtlayarak deforme eden türevsel denklemler türetilmiştir.
Bu denklemler kullanılarak eğrinin güncellenmesi ile imgedeki
kılavuz teline uygunluğu, pürüzsüzlüğü, ve telin uzunluğunun
korunması sağlanır. Analitik olarak türettiğimiz bu denklemler
önceki metotlardan farklı olarak teğetsel terimler de
içermektedir. X-ray videolarda tipik olarak karşılaşılan zayıf
kontrasta karşı imgeye bağlı öznitelik olarak faz eşlenmesi
haritası kullanılmıs¸tır. Geliştirilen metodun başarısı deneysel
sonuçlar ile düşük kontrastlı x-ray videoları ¨uzerinde kılavuz
teli izleme ile gösterilmis¸tir
Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels
This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature vector that contains intensity, shape index, and 3D spatial location. Then, nonparametric mean shift clustering forms superpixels from these 5D features, resulting in an oversegmentation of the image. Finally, a graph cut algorithm groups the superpixels using a novel energy formulation that incorporates shape, intensity, and spatial features. The mean shift superpixels increase the robustness of the result while reducing the computation time. We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion. From these spherical subregions, foreground and background seeds for the graph cut segmentation can be automatically obtained. The proposed method has been evaluated on a clinical CT dataset. Visual inspection on different types of lesions (lung nodules and colonic polyps), as well as a quantitative evaluation on 101 solid and 80 GGO nodules, both demonstrate the potential of the proposed method. The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect
Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
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Variational skinning of an ordered set of discrete 2D balls
This paper considers the problem of computing an interpolating
envelope of an ordered set of 2D balls. By construction, the envelope
is constrained to be C1 continuous, and for each ball, it touches the
ball at a point and is tangent to the ball at the point of contact. Using
an energy formulation, we derive differential equations that are designed
to minimize the envelope’s arc length and/or curvature subject to these
constraints. Given an initial envelope, we update the envelope’s parameters
using the differential equations until convergence occurs. We demonstrate
the method’s usefulness in generating interpolating envelopes of
balls of different sizes and in various configurations
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Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
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