Harmonic Active Contours

Estellers, Virginia; Zosso, Dominique; Bresson, Xavier; Thiran, Jean-Philippe

doi:10.1109/tip.2013.2286326

Cited by 23 publications

(11 citation statements)

References 40 publications

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“…Similar splitting techniques [11,12,31] have been applied to L 1 -based and related optimization problems with great success, such as [22,32]. The splitting constraint can be addressed, e.g., with a quadratic penalty (proximal splitting, [12]), or using an augmented Lagrangian [31].…”

Section: Model "Relaxation"mentioning

confidence: 99%

Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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Decomposing multidimensional signals, such as images, into spatially compact, potentially overlapping modes of essentially wavelike nature makes these components accessible for further downstream analysis. This decomposition enables space-frequency analysis, demodulation, estimation of local orientation, edge and corner detection, texture analysis, denoising, inpainting, or curvature estimation. Our model decomposes the input signal into modes with narrow Fourier bandwidth; to cope with sharp region boundaries, incompatible with narrow bandwidth, we introduce binary support functions that act as masks on the narrow-band mode for image recomposition. L 1 and TV terms promote sparsity and spatial compactness. Constraining the support functions to partitions of the signal domain, we effectively get an image segmentation model based on spectral homogeneity. By coupling several submodes together with a single support function, we are able to decompose an image into several crystal grains. Our efficient algorithm is based on variable splitting and alternate direction optimization; we employ Merriman-Bence-Osher-like threshold dynamics to handle efficiently the motion by mean curvature of the support function boundaries under the sparsity promoting terms. The versatility and effectiveness of our proposed model is demonstrated on a broad variety of example images from different modalities. These demonstrations include the decomposition of images into overlapping modes with smooth or sharp boundaries, segmentation of images of crystal grains, and inpainting of damaged image regions through artifact detection.

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Section: Model "Relaxation"mentioning

confidence: 99%

Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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“…Specifically, the alignment term attempts to align the normal vector of the zero level set with the image's gradient. Although this alignment term leads to more accurate segmentation results, the method may fail to accurately drive the zero level set to the desired boundary around weak edges due to the fact that the gradient of the image around weak edges is relatively small [31]. Belaid et al [19] propose a phase-based level set (PBLS) method to implement an active contour for segmentation of medical images with high levels of noise and weak edges.…”

Section: Introductionmentioning

confidence: 99%

“…PBLS has shown to perform very well in the presence of weak edges, despite requiring a careful tuning of the parameters associated with the edge map used by the method [43]. Estellers et al [31] propose a segmentation method based on the geometric representation of images as 2D manifolds embedded in a higher dimensional space. Their method, termed harmonic active contours (HAC), aligns the image's gradient with the gradient of the level set function for all the level sets.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the novelties of our approach are as follows: 1) Our method measures the alignment between the evolving contour's normal direction of movement and the image's gradient in the adjacent region located inside and outside of the evolving contour. Other methods that also measure this alignment, e.g., [29], [31], usually do this only in the adjacent region of the evolving contour in the direction of movement. Moreover, this measurement is often used as an additional energy term in the energy functional.…”

Section: Introductionmentioning

confidence: 99%

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Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Khadidos

Sánchez

2017

IEEE Trans. on Image Process.

167

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Permanent WRAP URL:http://wrap.warwick.ac.uk86204 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk JOURNAL OF L A T E X CLASS FILES 1 Weighted Level Set Evolution Based on Local Edge Features for Medical Image SegmentationAlaa Khadidos, Victor Sanchez, Member, IEEE, and Chang-Tsun Li, Senior Member, IEEE Abstract-Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of w...

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“…Soon after the snake model has been proposed, there has been a flurry of research devoted to the theory and application of this model. Generally speaking, the active contours can be categorized into region-based models [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and edge-based models [1], [21][22][23][24][25][26][27][28] according to how the image data is utilized. The region-based models usually employ certain region-homogeneity criteria to guide the evolution of the active contours, such as the local region descriptors in [17][18][19][20] and histogram in [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Virtual Electric Field for Image Segmentation Using Active Contours

et al. 2014

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Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

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Harmonic Active Contours

Cited by 23 publications

References 40 publications

Two-Dimensional Compact Variational Mode Decomposition

Two-Dimensional Compact Variational Mode Decomposition

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Convolutional Virtual Electric Field for Image Segmentation Using Active Contours

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