Medical image fusion based on shift-invariant shearlet transformation

作者：Wang, Lei; Li, Bin^*; Tian, Lian-Fang

来源：Journal of South China University of Technology(Natural Science Edition), 2011, 39(12): 13-19.

DOI：10.3969/j.issn.1000-565X.2011.12.003

• Image fusion methods
• Mutual informations
• Non-subsampled contourlet
• Pseudo Gibbs phenomena
• Self-generating neural networks
• Shearlet
• Shift invariance
• Transformation based

In the conventional shearlet transformation-based image fusion methods, there commonly exists a pseudo-Gibbs phenomenon at the singularities of the fused image. In order to solve this problem, a new fusion method of medical images is proposed based on the shift-invariant shearlet transformation. In this method, source images are decomposed into lowpass and highpass sub-bands via the shift-invariant shearlet transformation. Then, the lowpass coefficients are combined by employing the scheme based on the region coefficients'absolute values and weights, and the highpass sub-bands are merged by adopting a fusion scheme based on the support vector value-motivated self-generating neural network (SGNN). Finally, the fused image is obtained via the inverse shift-invariant shearlet transformation. Both the visual comparison and the quantitative analysis show that the proposed method effectively avoids the pseudo-Gibbs phenomenon and outperforms the conventional wavelet-based, contourlet-based and nonsubsampled contourlet-based methods in terms of entropy, mutual information, average gradient and QAB/F.

• 单位
  华南理工大学