5,700 research outputs found
Directional Geodesic Active Contours
We present a non-conformal metric that generalizes the geodesic active contours approach for image segmentation. The new metric is obtained by adding to the Euclidean metric an additional term that penalizes the misalignment of the curve with the image gradient and multiplying the resulting metric by a conformal factor that depends on the edge intensity. In this way, a closer fitting to the edge direction results. The provided experimental results address the computation of the geodesics of the new metric by applying a gradient descent to externally provided curves. The good performance of the proposed techniques is demonstrated in comparison with other active contours methods
Analysis of Amoeba Active Contours
Subject of this paper is the theoretical analysis of structure-adaptive
median filter algorithms that approximate curvature-based PDEs for image
filtering and segmentation. These so-called morphological amoeba filters are
based on a concept introduced by Lerallut et al. They achieve similar results
as the well-known geodesic active contour and self-snakes PDEs. In the present
work, the PDE approximated by amoeba active contours is derived for a general
geometric situation and general amoeba metric. This PDE is structurally similar
but not identical to the geodesic active contour equation. It reproduces the
previous PDE approximation results for amoeba median filters as special cases.
Furthermore, modifications of the basic amoeba active contour algorithm are
analysed that are related to the morphological force terms frequently used with
geodesic active contours. Experiments demonstrate the basic behaviour of amoeba
active contours and its similarity to geodesic active contours.Comment: Revised version with several improvements for clarity, slightly
extended experiments and discussion. Accepted for publication in Journal of
Mathematical Imaging and Visio
Asymmetric Geodesic Distance Propagation for Active Contours
This is the final version. Available from British Machine Vision Association (BMVA) via the link in this record. The dual-front scheme is a powerful curve evolution tool for active contours and image
segmentation, which has proven its capability in dealing with various segmentation
tasks. In its basic formulation, a contour is represented by the interface of two adjacent
Voronoi regions derived from the geodesic distance map which is the solution to
an Eikonal equation. The original dual-front model [17] is based on isotropic metrics,
and thus cannot take into account the asymmetric enhancements during curve evolution.
In this paper, we propose a new asymmetric dual-front curve evolution model through
an asymmetric Finsler geodesic metric, which is constructed in terms of the extended
normal vector field of the current contour and the image data. The experimental results
demonstrate the advantages of the proposed method in computational efficiency, robustness
and accuracy when compared to the original isotropic dual-front model.Roche pharmaAgence Nationale de la Recherch
Optimal Geodesic Active Contours: Application to Heart Segmentation
We develop a semiautomated segmentation method to assist in the analysis of functional pathologies of the left ventricle of the heart. The segmentation is performed using an optimal geodesic active contour with minimal structural knowledge to choose the most likely surfaces of the myocardium. The use of an optimal segmentation algorithm avoids the problems of contour leakage and false minima associated with variational active contour methods. The resulting surfaces may be analysed to obtain quantitative measures of the heart's function. We have applied the proposed segmentation method to multislice MRI data. The results demonstrate the reliability and efficiency of this scheme as well as its robustness to noise and background clutter
Globally optimal geodesic active contours
An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point p_int. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images
Applications of nonlinear diffusion in image processing and computer vision
Nonlinear diffusion processes can be found in many recent methods for image processing and computer vision. In this article, four applications are surveyed: nonlinear diffusion filtering, variational image regularization, optic flow estimation, and geodesic active contours. For each of these techniques we explain the main ideas, discuss theoretical properties and present an appropriate numerical scheme. The numerical schemes are based on additive operator splittings (AOS). In contrast to traditional multiplicative splittings such as ADI, LOD or D'yakonov splittings, all axes are treated in the same manner, and additional possibilities for efficient realizations on parallel and distributed architectures appear. Geodesic active contours lead to equations that resemble mean curvature motion. For this application, a novel AOS scheme is presented that uses harmonie averaging and does not require reinitializations of the distance function in each iteration step
Co-dimension 2 Geodesic Active Contours for MRA Segmentation
Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension
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