TubeTK/Anisotropic Hybrid Diffusion with Continuous Switch: Difference between revisions
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This algorithm is based on anisotropic non-linear diffusion. The technique combines edge-preserving noise reduction while enhancing local structures. This algorithm proposes hybrid approach to combine the advantages of EED ( Edge enhancing diffusion ) and CED ( Coherence enhancing diffusion ). | This algorithm is based on anisotropic non-linear diffusion. The technique combines edge-preserving noise reduction while enhancing local structures. This algorithm proposes hybrid approach to combine the advantages of EED ( Edge enhancing diffusion ) and CED ( Coherence enhancing diffusion ). | ||
EED focuses on edge preservation and enhancement. | EED focuses on edge preservation and enhancement. In EED, strong smoothing is applied along the direction of the edge while the strength of the smoothing along the other perpendicular directions depends on the gradient. The higher the gradient the lower the smoothing strength would be. | ||
On the other hand, CED is designed to to connect lines and improve flow-like structures and also enhance plane-like structure. | On the other hand, CED is designed to to connect lines and improve flow-like structures and also enhance plane-like structure. | ||
= Implementation = | = Implementation = | ||
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The tubetk implementation will follow ITK's finite difference solver framework. The implementation will be done as follows: | The tubetk implementation will follow ITK's finite difference solver framework. The implementation will be done as follows: | ||
# Implement structure tensor generation filter ( | # Implement structure tensor generation filter ( Second moment matrix that is currently not available in ITK ) | ||
# Edge-enhancing diffusion filter | # Edge-enhancing diffusion filter | ||
# Coherence-enhancing diffusion filter | # Coherence-enhancing diffusion filter | ||
# Hybrid diffusion filter | # Hybrid diffusion filter | ||
Revision as of 19:57, 5 April 2010
This module is an implementation of diffusion based smoothing technique developed by Mendrik et al.
Mendrik AM, Vonken EJ, Rutten A, Viergever MA, van Ginneken B. Noise reduction in computed tomography scans using 3-d anisotropic hybrid diffusion with continuous switch. IEEE Trans Med Imaging. 2009 Oct;28(10):1585-94.
Algorithm synopsis
This algorithm is based on anisotropic non-linear diffusion. The technique combines edge-preserving noise reduction while enhancing local structures. This algorithm proposes hybrid approach to combine the advantages of EED ( Edge enhancing diffusion ) and CED ( Coherence enhancing diffusion ).
EED focuses on edge preservation and enhancement. In EED, strong smoothing is applied along the direction of the edge while the strength of the smoothing along the other perpendicular directions depends on the gradient. The higher the gradient the lower the smoothing strength would be.
On the other hand, CED is designed to to connect lines and improve flow-like structures and also enhance plane-like structure.
Implementation
The tubetk implementation will follow ITK's finite difference solver framework. The implementation will be done as follows:
- Implement structure tensor generation filter ( Second moment matrix that is currently not available in ITK )
- Edge-enhancing diffusion filter
- Coherence-enhancing diffusion filter
- Hybrid diffusion filter
