Journal article 729 views
Divergence of Gradient Convolution: Deformable Segmentation with Arbitrary Initializations
IEEE Transactions on Image Processing, Volume: 24, Issue: 11, Pages: 3902 - 3914
Swansea University Author:
Xianghua Xie
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DOI (Published version): 10.1109/TIP.2015.2456503
Abstract
We propose a unified approach to deformable model based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes full use of directional information of the image gradient vectors and their interactions ac...
| Published in: | IEEE Transactions on Image Processing |
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| Published: |
2015
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa22242 |
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| Abstract: |
We propose a unified approach to deformable model based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes full use of directional information of the image gradient vectors and their interactions across image domain. The proposed external force field for deformable segmentation has both edge-based properties in that GCF is computed from image gradients, and region-based attributes since its divergence can be treated as a region indication function. Moreover, nonlinear diffusion can be conveniently applied to GCF to improve its performance in dealing with noise interference. We also show the extension of GCF from 2-D to 3-D. |
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| Keywords: |
Image segmentation, deformable model, initialisation invariance |
| College: |
Faculty of Science and Engineering |
| Issue: |
11 |
| Start Page: |
3902 |
| End Page: |
3914 |