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Convolutional virtual electric field for image segmentation using active contours.

Wang Y, Zhu C, Zhang J, Jian Y - PLoS ONE (2014)

Bottom Line: Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load.Meanwhile, the CONVEF model can also be implemented in real-time by using FFT.Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Tianjin University of Technology, Tianjin, China.

ABSTRACT
Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

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Related in: MedlinePlus

Segmentation of the human lung CT image using (a) VFC snake with , (b) VEF snake, (c) CONVEF snake with n = 1.5, h = 0.0, and (d) CONVEF snake with n = 3.0, h = 5.0.
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pone-0110032-g010: Segmentation of the human lung CT image using (a) VFC snake with , (b) VEF snake, (c) CONVEF snake with n = 1.5, h = 0.0, and (d) CONVEF snake with n = 3.0, h = 5.0.

Mentions: Fig. 10 shows the segmentation results of the VFC, VEF and CONVEF snakes on a human lung CT image. The purpose of this example is to extract the parenchyma in the left part and the cancer in the right part, and the difficulties reside in the weak edge and closely-neighboring boundaries. The results of VFC and VEF snakes are shown in Figs. 10 (a) and (b), respectively, and the convergent contours of both snakes leak out although the VEF snake behaves much better than the VFC snake. Figs. 10 (c) and (d) show the results of the CONVEF snakes with different parameter settings. Once again this experiment exemplifies the abilities of the CONVEF snake for weak edge preserving and neighboring objects separation.


Convolutional virtual electric field for image segmentation using active contours.

Wang Y, Zhu C, Zhang J, Jian Y - PLoS ONE (2014)

Segmentation of the human lung CT image using (a) VFC snake with , (b) VEF snake, (c) CONVEF snake with n = 1.5, h = 0.0, and (d) CONVEF snake with n = 3.0, h = 5.0.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4216009&req=5

pone-0110032-g010: Segmentation of the human lung CT image using (a) VFC snake with , (b) VEF snake, (c) CONVEF snake with n = 1.5, h = 0.0, and (d) CONVEF snake with n = 3.0, h = 5.0.
Mentions: Fig. 10 shows the segmentation results of the VFC, VEF and CONVEF snakes on a human lung CT image. The purpose of this example is to extract the parenchyma in the left part and the cancer in the right part, and the difficulties reside in the weak edge and closely-neighboring boundaries. The results of VFC and VEF snakes are shown in Figs. 10 (a) and (b), respectively, and the convergent contours of both snakes leak out although the VEF snake behaves much better than the VFC snake. Figs. 10 (c) and (d) show the results of the CONVEF snakes with different parameter settings. Once again this experiment exemplifies the abilities of the CONVEF snake for weak edge preserving and neighboring objects separation.

Bottom Line: Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load.Meanwhile, the CONVEF model can also be implemented in real-time by using FFT.Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Tianjin University of Technology, Tianjin, China.

ABSTRACT
Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

Show MeSH
Related in: MedlinePlus