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Multi-modal Medical Image Fusion Based on Non-subsampled Shearlet Transform
Xing Xiaoxue,Cao Fucheng,Shang Weiwei,Liu Fu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.2
In order to provide more comprehensive and effective information for cancer diagnosis and tumor treatment planning, it is necessary to fuse multi-modal medical images, such as CT/MRI image, CT/PET image, MRI/SPECT image and so on. In this paper, a multi-modal medical image fusion method based on Non-subsampled Shearlet Transform is proposed. Firstly, in this method, source images are decomposed into low-pass and high-pass subbands by NSST. Then, due to the characteristic features--large sparsity and strong contrast, the high-frequency and low-frequency coefficients of the images are fused by the different fusion rules. Finally, the image is reconstructed by the inverse non-sampled shearlet transform. In the method, the fusion rules are designed based on the regional energy and the average gradient; the image entropy, relative quality, average gradient, standard deviation and spatial frequency were used to evaluate the fusion results objectively. In the experiments, CT and MRI images are chosen to verify the method. Both the visual and the objective analysis show that the proposed method is better than the conventional Wavelet-based and non-subsampled Contourlet-based methods.
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