A New Fusion Framework for Multimodal Medical Image Based on GRWT

Hypertension is one of the most important contributors to heart disease and stroke. Multimodality medical image fusion plays an important role in the precise diagnosis, treatment planning and follow-up studies of various diseases. In this paper, we propose an image fusion framework in patients with hypertension, which is based on Frei-Chen operators in generalized reisz-wavelet transform (GRWT) domain. The proposed method is tested on two cases of MRI/CT and MRI/SPECT images. The input medical images are first transformed by GRWT into basic and detail components. Further, they are fused respectively by Frei-Chen operators which extracts ripples, edges, lines and points well. Finally, the fused image is constructed by the inverse GRWT and evaluated by indicators such as average gradient and spatial frequency etc. The visual and quantitative evaluation of the results demonstrated the superior performance of the proposed image fusion method which assists doctors for precise and sufficient diagnosis.