Exploiting local intensity information in Chan–Vese model for noisy image segmentation

Liu, Linghui; Zeng, Li; Shen, Kuan; Luan, Xiao

doi:10.1016/j.sigpro.2013.03.035

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…C-V model is based on the assumption that the image is formed by two regions of approximately piecewise-constant intensities, and it can segment an object whose boundary is not necessary by gradient [ 15 ]. However, since C-V model only employs global intensity information of the image to detect boundary, it is difficult to segment cracks in the CT image of rock core, area of which is very small and the gray level of which is close to the gray level of the rock core.…”

Section: Introductionmentioning

confidence: 99%

Research on 3D crack segmentation of CT images of oil rock core

2021

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In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

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Section: Introductionmentioning

confidence: 99%

Research on 3D crack segmentation of CT images of oil rock core

2021

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“…In this paper, a region-based ACM to segment noisy color images in a much more efficient manner is proposed. The idea of incorporating localized statistics into a variational framework, proposed firstly by Lankton and Tannenbaum [ 10 ], makes contribution to the robustness to noise [ 15 ]. The proposed model measures both the global and local statistical properties of the object to adjust the level set function and fit the object with the zero level set, which is found to improve the robustness to strong noise and hold global dependence greatly.…”

Section: Introductionmentioning

confidence: 99%

Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

Xie

Zhang

Wang

et al. 2014

The Scientific World Journal

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A narrow band active contour model for color image segmentation is proposed, which applies local statistics to improve the robustness against noise. The crux of our approach is to use intensity mean of local region to define the force function within a level set framework, within which a narrow band is implemented to further improve the computational efficiency. In addition, the image is segmented from channel-to-channel, which shows superior performance when the intensities of the object and background are similar. Furthermore, a multichannel segmentation combination method is used to integrate the information of multiple level sets. The proposed model has been applied to both synthetic and real images with expected results, and the comparison with the state-of-the-art approaches demonstrates the accuracy and superiority of our approach.

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Speed Parameters in the Level-Set Segmentation

Cinque

Cossu

2015

Computer Analysis of Images and Patterns

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Exploiting local intensity information in Chan–Vese model for noisy image segmentation

Cited by 11 publications

References 27 publications

Research on 3D crack segmentation of CT images of oil rock core

Research on 3D crack segmentation of CT images of oil rock core

Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

Speed Parameters in the Level-Set Segmentation

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