AI Article Synopsis
- The paper introduces a new nonlinear diffusion technique for enhancing angiogram images, aiming to support better clinical diagnoses.
- It addresses limitations of previous models, such as dependence on parameters for edge enhancement and noise reduction, by using a facet model that adapts based on the image content.
- Experimental results demonstrate that this new method improves image quality more effectively than earlier anisotropic diffusion methods.
Article Abstract
The paper presents a versatile nonlinear diffusion method to visually enhance the angiogram images for improving the clinical diagnosis. Traditional nonlinear diffusion has been shown very effective in edge-preserved smoothing of images. However, the existing nonlinear diffusion models suffer several drawbacks: sensitivity to the choice of the conductance parameter, limited range of edge enhancement, and the sensitivity to the selection of evolution time. The new anisotropic diffusion we proposed is based on facet model which can solve the issues mentioned above adaptively according to the image content. This method uses facet model for fitting the image to reduce noise, and uses the sum square of eigenvalues of Hessian as the standard of the conductance parameter selection synchronously. The capability of dealing with noise and conductance parameter can also change adaptively in the whole diffusion process. Moreover, our method is not sensitive to the choice of evolution time. Experimental results show that our new method is more effective than the original anisotropic diffusion.
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| http://dx.doi.org/10.1016/j.compmedimag.2008.11.001 | DOI Listing |
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