A convex variational level set model for image segmentation

Wu, Yongfei; He, Chu

doi:10.1016/j.sigpro.2014.07.013

Cited by 68 publications

(89 citation statements)

References 21 publications

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“…In the variational level set methods [10,11,[22][23][24], the evolution problems of contour curve are converted to the minimization problem of the energy functions including different image feature information, and then are solved by the gradient descent flow method and the variational method. Therefore, the level set evolution equation of contour curve can be obtained as…”

Section: Principle Of Level Set Methodsmentioning

confidence: 99%

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An improved edge-based level set method combining local regional fitting information for noisy image segmentation

2017

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Section: Principle Of Level Set Methodsmentioning

confidence: 99%

“…In the variational level set methods [11,[22][23][24], the temporal partial derivatives are approximated by the forward difference equation and the spatial partial derivatives are approximated by the central difference equation, thus the level set evolution equations of (8) can be discretized as…”

Section: Principle Of Level Set Methodsmentioning

confidence: 99%

An improved edge-based level set method combining local regional fitting information for noisy image segmentation

2017

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“…Image segmentation is fundamental step for computer vision, pattern recognition, and medical image processing [1]. The main objective of image segmentation is to divide an image automatically or semi-automatically into non-overlapping regions, in such a way each region is homogeneous with respect to some characteristics such as intensity or texture [2].…”

Section: Introductionmentioning

confidence: 99%

CPU and GPU behaviour modelling versus sequential and parallel bias field correction fuzzy C-means algorithm implementations

Cherradi¹,

Ali²,

Abbassi³

et al. 2017

ces

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The correction of images corrupted by bias field artefact is still challenging task both at accuracy level as on the computational plane. The work in this paper focus on the second constraint by giving mathematical models of experimental execution time per iteration ETPI(s) on GPU and CPU implementations and speed-ups GPU/CPU(x) of the iterative Bias Field Correction Fuzzy C-means clustering Algorithm (Both sequential BCFCM and parallel PBCFCM versions) against the variable cluster number. In this study we characterize the behaviour of these devices against the proposed implementations to show the optimal situation that offers the best performance. The modelling results of cluster number variation show interesting behaviours and are in good accordance to experimental ones. 424 Bouchaib Cherradi et al.

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“…The results of segmentation are not always satisfactory because of low contrast, blurry boundaries, noise, and inhomogeneous intensities. Hence, image segmentation is still a quite difficult task [1].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Level Set Approach for Image Contour Detection

Zhang¹,

Xu²,

Cheng³

2016

Mathematical Problems in Engineering

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The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

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A convex variational level set model for image segmentation

Cited by 68 publications

References 21 publications

An improved edge-based level set method combining local regional fitting information for noisy image segmentation

An improved edge-based level set method combining local regional fitting information for noisy image segmentation

CPU and GPU behaviour modelling versus sequential and parallel bias field correction fuzzy C-means algorithm implementations

A Novel Fuzzy Level Set Approach for Image Contour Detection

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