Abstract:

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.

Key words: medical image, image fusion, shift invariance, shearlet, discrete shearlet transformation