Adaptive level set evolution starting with a constant function

Wang, Yan; He, Chu

doi:10.1016/j.apm.2011.10.023

Cited by 28 publications

(36 citation statements)

References 21 publications

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“…Wang et al [16] proposed an adaptive level set evolution starting with a constant function for image segmentation. This model is formulated in PDE framework on the level set function as follows:…”

Section: Wang's Modelmentioning

confidence: 99%

“…More recently, Wang et al [16] proposed an adaptive level set evolution starting with a constant function. This model solves the problem of the initial contours and has been successfully applied to some synthetic and real images.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the work [16], based on Chan-Vese model and LIF model, this paper proposes a novel active contours model in a partial differential equation (PDE) formulation. The evolution equation consists of two terms: a force term and a regularization term that smoothes the level set function.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive active contours based on local and global intensity information for image segmentation

Wen

2014

Optik

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Section: Wang's Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive active contours based on local and global intensity information for image segmentation

Wen

2014

Optik

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“…This property generates serious difficulty because the local minima of the energy functional often provide poor segmentation results. After Chan and Vese's work, different models that are based on the Mumford-Shah functional with level-set methods have been developed and widely adopted in various image applications (Aldo et al 2008;Chan and Vese 2001;Li et al 2005Li et al , 2010Zhang et al 2010;Wang and He 2011;Wu and He 2015). The original idea of the level set methods is to implicitly represent an interface as the zero level set of a function in a higher dimension, which is called a level set function, and then the level set function is deformed according to an evolutionary partial differential equation.…”

Section: Introductionmentioning

confidence: 99%

A novel method for image segmentation using reaction–diffusion model

Wen

Zhang

et al. 2015

Multidim Syst Sign Process

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We propose an image segmentation model that is derived from reaction-diffusion equations and level set methods. In our model, a diffusion term is used for regularization of a level set function, and a reaction term has the desired sign property to force the level set function to move up or down and finally identify an object and its background. Our level set function can be initialized to any bounded function (e.g., a constant function). The proposed model can be applied to a wider range of images with promising results, especially for real images that have high noise and blurred boundaries. This study gives a new method for the further investigations of reaction-diffusion equations directly for segmentation.

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“…Traditional methods, like Canny edge detection [7], are simple and fast, but they always need further edge linking operation to produce continuous object boundaries [9]. To address these issues, more recent methods including implicit active contours [9][10][11][12][13][14][15][16][17] have been developed for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation algorithm by piecewise smooth approximation

Wang

2012

J Image Video Proc

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We propose a novel image segmentation algorithm using piecewise smooth (PS) approximation to image. The proposed algorithm is inspired by four well-known active contour models, i.e., Chan and Vese' piecewise constant (PC)/smooth models, the region-scalable fitting model, and the local image fitting model. The four models share the same algorithm structure to find a PC/smooth approximation to the original image; the main difference is how to define the energy functional to be minimized and the PC/smooth function. In this article, pursuing the same idea we introduce different energy functional and PS function to search for the optimal PS approximation of the original image. The initial function with our model can be chosen as a constant function, which implies that the proposed algorithm is robust to initialization or even free of manual initialization. Experiments show that the proposed algorithm is very appropriate for a wider range of images, including images with intensity inhomogeneity and infrared ship images with low contrast and complex background.

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Adaptive level set evolution starting with a constant function

Cited by 28 publications

References 21 publications

Adaptive active contours based on local and global intensity information for image segmentation

Adaptive active contours based on local and global intensity information for image segmentation

A novel method for image segmentation using reaction–diffusion model

Image segmentation algorithm by piecewise smooth approximation

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