Multimodality Medical Image Fusion Based On Multiscale Geometric Analysis Of Contourlet Transform

摘要

As a novel multiscale geometric analysis tool, contourlet has shown many advantages over the conventional image representation methods. In this paper, a new fusion algorithm for multimodal medical images based on contourlet transform is proposed. All fusion operations are performed in contourlet domain. A novel contourlet contrast measurement is developed, which is proved to be more suitable for human vision system. Other fusion rules like local energy, weighted average and selection are combined with "region" idea for coefficient selection in the lowpass and highpass subbands, which can preserve more details in source images and further improve the quality of fused image. The final fusion image is obtained by directly applying inverse contourlet transform to the fused lowpass and highpass subbands. Extensive fusion experiments have been made on three groups of multimodality CT/MR dataset, both visual and quantitative analysis show that comparing with conventional image fusion algorithms, the proposed approach can provide a more satisfactory fusion outcome.