Implicit Active Contours Driven by Local Binary Fitting Energy

Li, Chunming; Kao, Chiu‐Yen; Gore, John C.; Ding, Zhaohua

doi:10.1109/cvpr.2007.383014

Cited by 751 publications

(712 citation statements)

References 11 publications

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“…This diffusion scheme requires the iterative resolution of a PDE with new boundary conditions at each update step (B), which is computationnally costly. Methods based on Gaussian convolution were recently proposed [19][20][21], giving qualitatively similar results in a more efficient way. They enable the extrapolation of the model beyond the region boundary, to an extent that depends on the kernel's scale.…”

Section: Region Intensity Modelsmentioning

confidence: 53%

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

Somphone

Mory

Makram–Ebeid

et al. 2008

Lecture Notes in Computer Science

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Abstract. We propose a new algorithm for two-phase, piecewise-smooth segmentation with shape prior. The image is segmented by a binary template that is deformed by a regular geometric transformation. The choice of the template together with the constraint on the transformation introduce the shape prior. In particular, the topology of the shape is preserved if the transformation is diffeomorphic. The deformation is guided by the maximization of the likelihood of foreground and background intensity models, so that we can refer to this approach as Competitive Deformation. In each region, the intensity is modelled as a smooth approximation of the original image. We represent the transformation using a Partition of Unity Finite Element Method, which consists in representing each component with polynomial approximations within local patches. A conformity constraint between the patches provides a way to control the globality of the deformation. We show several results on synthetic images, as well as on medical data from different modalities.

show abstract

Section: Region Intensity Modelsmentioning

confidence: 53%

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

Somphone

Mory

Makram–Ebeid

et al. 2008

Lecture Notes in Computer Science

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show abstract

“…Fig. 1 shows the segmentation results for three real blood vessel images with inhomogeneous intensity via use of the the Local Binary Fitting mode [12], Local Chan-Vese model [16] and the proposed method. In [16] they had compared the CV model with the LCV model.…”

Section: Expriment Resultsmentioning

confidence: 99%

“…Inside each region, the original image is approximated by a smooth function. Since the great success of curve evolution [4] and level-set method [5] Mumford-Shah type model, different approaches have been tried to apply such method to image segmentation [6]- [12]. For example, Chan and Vese(CV) [6] proposed a level-set framework to minimize the so-called piecewise constant model by assuming that an image consists of statistically homogeneous regions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Branched Blood Vessels Segmentation Based on Phase-Field and Statistical Model

Zhao¹,

Zhou²,

Wu³

et al. 2013

IJBBB

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Abstract-The precise segmentation of cerebral vessels is essential for the detection of cerebrovascular diseases. The complex structures of cerebral vessels and the low contranst of thin vessels in medical images make precise segmentation difficult. In this study, we propose a new phase-field and statistical model for blood vessel segmentation. The proposed model is based on the Allen-Chan equation with double well potential and statistical distribution function. The brain tissues in the image are modeled by Gaussian distribution while cerebral vessels are modeled by uniform distribution respectively. The region distribution information combined with the phase-field model is used in curve evolution. And the level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation.Comparisons with the LBF model and LCV model show that our model can achieve better results with fewer iteration number and less time.Index Terms-Segmentation, cerebral blood vessel, intensity inhomogeneity, statistical model.

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“…They require the estimation of a piecewise smooth parameter field, see for instance [5][6][7][8][9]. The non-local active contour method, recently introduced in [10,11], also makes use of such a local homogeneity principle, but implements the variational minimization using only a pairwise comparison of features, which might for instance depend on patches extracted around each pixel.…”

Section: Previous Workmentioning

confidence: 99%

Texture Segmentation via Non-local Non-parametric Active Contours

Jung

Peyré

Cohen

2011

Lecture Notes in Computer Science

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Abstract. This article introduces a novel active contour model that makes use of non-parametric estimators over patches for the segmentation of textured images. It is based on an energy that enforces the homogeneity of these statistics. This smoothness is measured using Wasserstein distances among discretized probability distributions that can handle features in arbitrary dimension. It is thus usable for the segmentation of color images or other high dimensional features. The Wasserstein distance is more robust than traditional pointwise statistical metrics (such as the Kullback-Leibler divergence) because it takes into account the relative distances between modes in the distributions. This makes the corresponding energy robust and does not require any smoothing of the statistical estimators. To speed-up the computational time, we propose an alternative metric that retains the main qualities of the Wasserstein distance, while being faster to compute. It aggregates 1-D Wasserstein distances over a set of directions, and thus benefits from the simplicity of 1-D statistical metrics while being able to discriminate high dimensional features. We show numerical results that instantiate this novel framework using grayscale and color values distributions. This allows us to segment regions with smoothly varying intensities or colors as well as complicated textures.

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Implicit Active Contours Driven by Local Binary Fitting Energy

Cited by 751 publications

References 11 publications

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

Multi-Branched Blood Vessels Segmentation Based on Phase-Field and Statistical Model

Texture Segmentation via Non-local Non-parametric Active Contours

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