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Paragios, Nikos; Deriche, Rachid

doi:10.1023/a:1014080923068

Cited by 651 publications

(15 citation statements)

References 88 publications

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“…In traditional texture segmentation methods, the Gabor filtering 20,38 or patch 39,40 are often used to segment texture images. In traditional texture segmentation methods, the Gabor filtering 20,38 or patch 39,40 are often used to segment texture images.…”

Section: Effect Of Force Armsmentioning

confidence: 99%

“…Sum and Cheung 15 proposed a novel model that combines the global and local information with a pair of balanced parameters. [19][20][21] In a word, some region-based methods 6-10 rely on the global region intensity information while the methods in Refs. Wang et al 16 proposed the local Chan Vese (LCV) model, which can segment images with intensity inhomogeneity, and proposed solving the texture image segmentation by constructing the extended structure tensor (EST).…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to segment the texture images. Some region-based methods [15][16][17] tried to incorporate both global and local intensity information into the energy functional construction, and other methods [18][19][20][21] considered including the structure tensor and filtering information. To further segment texture images, Wang et al 18 proposed incorporating intensity and local geometric features (orientation and gradient) into a tensor based level set model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Level set method for image segmentation based on moment competition

Min

Huang

Jin

et al. 2015

J. Electron. Imaging

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We propose a level set method for image segmentation which introduces the moment competition and weakly supervised information into the energy functional construction. Different from the region-based level set methods which use force competition, the moment competition is adopted to drive the contour evolution. Here, a so-called three-point labeling scheme is proposed to manually label three independent points (weakly supervised information) on the image. Then the intensity differences between the three points and the unlabeled pixels are used to construct the force arms for each image pixel. The corresponding force is generated from the global statistical information of a region-based method and weighted by the force arm. As a result, the moment can be constructed and incorporated into the energy functional to drive the evolving contour to approach the object boundary. In our method, the force arm can take full advantage of the three-point labeling scheme to constrain the moment competition. Additionally, the global statistical information and weakly supervised information are successfully integrated, which makes the proposed method more robust than traditional methods for initial contour placement and parameter setting. Experimental results with performance analysis also show the superiority of the proposed method on segmenting different types of complicated images, such as noisy images, threephase images, images with intensity inhomogeneity, and texture images.

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Section: Effect Of Force Armsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Level set method for image segmentation based on moment competition

Min

Huang

Jin

et al. 2015

J. Electron. Imaging

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show abstract

“…First, LS are able to increase robustness of the model by combining both region and boundary segmentation. Second, the texture and shape priors are jointly used with a continuous parametric function to model the implicit segmentation function (Bernard et al, 2007;Cremers et al, 2006;Lin et al, 2003;Lynch et al, 2008;Paragios and Deriche, 2002;Sarti et al, 2005;Debreuve et al, 2001;Paragios, 2003).…”

Section: Level Set Segmentation Approachesmentioning

confidence: 99%

Segmentation Methods of Echocardiography Images for Left Ventricle Boundary Detection

Mazaheri

Rahmat

Sulaiman

et al. 2015

Journal of Computer Science

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Due to acoustic interferences and artifacts which are inherent in echocardiography images, automatic segmentation of anatomical structures in cardiac ultrasound images is a real challenge. This paper surveys stateof-the-art researches on echocardiography data segmentation methods, concentrating on methods techniques developed for clinical data. We present a classification of methodologies for echocardiography image segmentation. By choosing ten recent papers which have proposed innovative ideas that they proved certain clinical advantages or potential especial role to the echocardiography segmentation task. The contribution of the paper would be serving as a tutorial of the field for both clinicians and technologists, providing large number of segmentation techniques in a comprehensive and systematic manner and critically review recent approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis.

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“…In addition, Caselles and Catte et al [2] discussed geometric model based on the geodetic active contours, it can extract the smooth shape target and test a few outlines at the same time. Caselles, Kimmel et al [3] and N. Paragios, R. Deriche et al [4] promoted the geodetic active contour model based on the active contours can evolve the outline splitting and merging naturally along with the time changes and detect the internal and external boundaries for many objects according to the image internal geometry measures. These methods are efficiently, but there still exist some difficulties in the computation.…”

Section: Introductionmentioning

confidence: 99%

Improve the Nonparametric Image Segmentation with Narrowband Levelset and Fast Gauss Transformation

Jiang¹

2012

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Nonparametric method based on the mutual information is an efficient technique for the image segmentation. In this method, the image is divided into the internal and external labeled regions, and the segmentation problem constrained by the total length of the region boundaries will be changed into the maximization of the mutual information between the region labels and the image pixel intensities. The maximization problem can be solved by deriving the associated gradient flows and the curve evolutions. One of the advantages for this method does not need to choose the segmentation parameter; another is not sensitive to the noise. By employing the narrowband levelset and Fast Gauss Transformation, the computation time is reduced clearly and the algorithm efficiency is greatly improved.

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Untitled

Cited by 651 publications

References 88 publications

Level set method for image segmentation based on moment competition

Level set method for image segmentation based on moment competition

Segmentation Methods of Echocardiography Images for Left Ventricle Boundary Detection

Improve the Nonparametric Image Segmentation with Narrowband Levelset and Fast Gauss Transformation

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