Noval Wavelet Coefficient for Fusion of Multi Modality Images and Preserving Edge Features in Medical Applications

Most of previous image fusion methods aim at obtaining as many as information from the different modality images. The fusion criterion is to minimize different error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the interested objects is more important than other information. Therefore, how to preserve the edge-like features is worthy of investigating for medical image fusion. As we know, the image with higher contrast contain more edge-like features. In term of this view, we proposed a novel medical image fusion scheme based on an improved wavelet coefficient contrast, which is defined as the ratio of the maximum of detail components to the local mean of the corresponding approximate component. The visual experiments and quantitative assessments demonstrate the effectiveness of this method compared to present image fusion schemes, especially for medical diagnosis.