Active contours with neighborhood-extending and noise-smoothing gradient vector flow external force

Liu, Lixiong; Bovik, Alan C.

doi:10.1186/1687-5281-2012-9

Cited by 8 publications

(9 citation statements)

References 16 publications

(20 reference statements)

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“…In [10], we already know the NNGVF has a much better performance on weak edges preserving than GVF, because of their different Laplacian operator. However, the NNGGVF maintains this desirable property.…”

Section: B Weak Edges Preservingmentioning

confidence: 98%

“…This model is what we call NNGVF [10] model. It is defined as the equilibrium solution of the following vector partial differential equation:…”

Section: Rm Nsmentioning

confidence: 98%

“…Various improved models based on the GVF model have been developed. For instance, generalized gradient flow (GGVF) [9], neighborhood-extending and noisesmoothing gradient vector flow (NNGVF) [10].…”

Section: Introductionmentioning

confidence: 99%

“…[9][10], we propose a novel external force for active contour models named neighborhoodextending and noise-smoothing generalized gradient vector flow (NNGGVF). The NNGGVF not only has a large capture range and an ability to converge into long, thin concavities, but also maintains other desirable properties of the NNGVF and GGVF snakes such as low noise sensitivity and weak edges preserving.…”

Section: Introductionmentioning

confidence: 99%

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Active Contours with Neighborhood-Extending and Noise-Smoothing Generalized Gradient Vector Flow External Force

Wang¹,

Wang²,

Zhou³

et al. 2013

2013 International Conference on Information Technology and Applications

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The recently proposed Neighborhood-extending and Noise-smoothing Gradient Vector Flow (NNGVF) provides a better segmentation to images than the GVF in terms of noise resistance, weak edges preservation. However, the NNGVF snake still has difficulties converging into long, thin boundary indentations. In this paper, we propose a novel external force for active contour models named NNGGVF which is a generalization of the NNGVF include two spatially varying weighting functions. It improves snake's ability of convergence into long, thin boundary indentations while maintaining other desirable properties of the NNGVF, such as better noise immunity and enlarged capture range. We demonstrate the advantages of the NNGGVF on synthetic and real images.

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Section: B Weak Edges Preservingmentioning

confidence: 98%

“…This model is what we call NNGVF [10] model. It is defined as the equilibrium solution of the following vector partial differential equation:…”

Section: Rm Nsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active Contours with Neighborhood-Extending and Noise-Smoothing Generalized Gradient Vector Flow External Force

Wang¹,

Wang²,

Zhou³

et al. 2013

2013 International Conference on Information Technology and Applications

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“…Tan, Schwartz and Zhao in [24], combined image processing techniques of marker-controlled watershed, geometric active contours as well as the Markov random field to evaluate lung lesions considering their size, density, and shape. Finally, Liu and Bovik in [25], proposed a novel external force for active contours, which we call neighborhood-extending and noise-smoothing gradient vector flow to extract the parenchyma and cancer regions.…”

Section: Introductionmentioning

confidence: 99%

3D segmentation and visualization of lung and its structures using CT images of the thorax

Filho¹,

Cortez²,

Albuquerque³

2013

JBiSE

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Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

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Automatic Detection of Contours of Circular Geologic Structures on Active Remote Sensing Images Using the Gradient Vector Flow Active Contour

Mokadem

Amine

2013

Modeling Approaches and Algorithms for Advanced Computer Applications

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Active contours with neighborhood-extending and noise-smoothing gradient vector flow external force

Cited by 8 publications

References 16 publications

Active Contours with Neighborhood-Extending and Noise-Smoothing Generalized Gradient Vector Flow External Force

Active Contours with Neighborhood-Extending and Noise-Smoothing Generalized Gradient Vector Flow External Force

3D segmentation and visualization of lung and its structures using CT images of the thorax

Automatic Detection of Contours of Circular Geologic Structures on Active Remote Sensing Images Using the Gradient Vector Flow Active Contour

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