X-ray angiogram images enhancement by facet-based adaptive anisotropic diffusion.
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.
Wang
Guodong
G
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074 Hubei, China.
Sang
Nong
N
Yan
Luxin
L
Shen
Xubang
X
D000792:Angiography / Q000379:methods*; D016880:Anisotropy; D016477:Artifacts; D003331:Coronary Vessels / Q000033:anatomy & histology; D004735:Energy Transfer; D017143:Fuzzy Logic; D006801:Humans; D008961:Models, Structural; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted